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Wiltgen T, McGinnis J, Schlaeger S, Kofler F, Voon C, Berthele A, Bischl D, Grundl L, Will N, Metz M, Schinz D, Sepp D, Prucker P, Schmitz-Koep B, Zimmer C, Menze B, Rueckert D, Hemmer B, Kirschke J, Mühlau M, Wiestler B. LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation. medRxiv 2024:2023.11.23.23298966. [PMID: 38045345 PMCID: PMC10690346 DOI: 10.1101/2023.11.23.23298966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10mm3 and 100mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.
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Affiliation(s)
- Tun Wiltgen
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julian McGinnis
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - CuiCi Voon
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daria Bischl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lioba Grundl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nikolaus Will
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dominik Sepp
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benita Schmitz-Koep
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Daniel Rueckert
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Bernhard Hemmer
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Munich, Germany
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Kazerooni AF, Khalili N, Liu X, Haldar D, Jiang Z, Anwar SM, Albrecht J, Adewole M, Anazodo U, Anderson H, Bagheri S, Baid U, Bergquist T, Borja AJ, Calabrese E, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Familiar A, Farahani K, Haldar S, Iglesias JE, Janas A, Johansen E, Jones BV, Kofler F, LaBella D, Lai HA, Leemput KV, Li HB, Maleki N, McAllister AS, Meier Z, Menze B, Moawad AW, Nandolia KK, Pavaine J, Piraud M, Poussaint T, Prabhu SP, Reitman Z, Rodriguez A, Rudie JD, Sanchez-Montano M, Shaikh IS, Shah LM, Sheth N, Shinohara RT, Tu W, Viswanathan K, Wang C, Ware JB, Wiestler B, Wiggins W, Zapaishchykova A, Aboian M, Bornhorst M, de Blank P, Deutsch M, Fouladi M, Hoffman L, Kann B, Lazow M, Mikael L, Nabavizadeh A, Packer R, Resnick A, Rood B, Vossough A, Bakas S, Linguraru MG. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). ArXiv 2024:arXiv:2305.17033v6. [PMID: 37292481 PMCID: PMC10246083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
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Weidner J, Ezhov I, Balcerak M, Metz MC, Litvinov S, Kaltenbach S, Feiner L, Lux L, Kofler F, Lipkova J, Latz J, Rueckert D, Menze B, Wiestler B. A Learnable Prior Improves Inverse Tumor Growth Modeling. ArXiv 2024:arXiv:2403.04500v1. [PMID: 38495563 PMCID: PMC10942480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95.
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Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Yaniv ZR, Jäger PF, Maier-Hein L. Understanding metric-related pitfalls in image analysis validation. Nat Methods 2024; 21:182-194. [PMID: 38347140 DOI: 10.1038/s41592-023-02150-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.
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Affiliation(s)
- Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Quebec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Dept of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Goethe University Frankfurt, Department of Medicine, Frankfurt am Main, Germany
- Goethe University Frankfurt, Department of Informatics, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- Universitat Pompeu Fabra, Barcelona, Spain
- University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | - Jens Kleesiek
- Translational Image-guided Oncology (TIO), Institute for AI in Medicine (IKIM), University Medicine Essen, Essen, Germany
| | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Quebec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | | | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- UiT The Arctic University of Norway, Tromsø, Norway
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Ziv R Yaniv
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
| | - Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany.
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5
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Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF. Metrics reloaded: recommendations for image analysis validation. Nat Methods 2024; 21:195-212. [PMID: 38347141 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
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Affiliation(s)
- Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Medicine Essen, Essen, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manuel Wiesenfarth
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Québec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, IU Health Information and Translational Sciences Building, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Matthew B Blaschko
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
- Australian Institute for Machine Learning AIML, University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Robert Haase
- Technische Universität (TU) Dresden, DFG Cluster of Excellence 'Physics of Life', Dresden, Germany
- Center for Systems Biology, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Peter Mattson
- Google, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Québec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
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6
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Yang K, Musio F, Ma Y, Juchler N, Paetzold JC, Al-Maskari R, Höher L, Li HB, Hamamci IE, Sekuboyina A, Shit S, Huang H, Waldmannstetter D, Kofler F, Navarro F, Menten M, Ezhov I, Rueckert D, Vos I, Ruigrok Y, Velthuis B, Kuijf H, Hämmerli J, Wurster C, Bijlenga P, Westphal L, Bisschop J, Colombo E, Baazaoui H, Makmur A, Hallinan J, Wiestler B, Kirschke JS, Wiest R, Montagnon E, Letourneau-Guillon L, Galdran A, Galati F, Falcetta D, Zuluaga MA, Lin C, Zhao H, Zhang Z, Ra S, Hwang J, Park H, Chen J, Wodzinski M, Müller H, Shi P, Liu W, Ma T, Yalçin C, Hamadache RE, Salvi J, Llado X, Maria Lal-Trehan Estrada U, Abramova V, Giancardo L, Oliver A, Liu J, Huang H, Cui Y, Lin Z, Liu Y, Zhu S, Patel TR, Tutino VM, Orouskhani M, Wang H, Mossa-Basha M, Zhu C, Rokuss MR, Kirchhoff Y, Disch N, Holzschuh J, Isensee F, Maier-Hein K, Sato Y, Hirsch S, Wegener S, Menze B. TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA. ArXiv 2024:arXiv:2312.17670v2. [PMID: 38235066 PMCID: PMC10793481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.
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Affiliation(s)
- Kaiyuan Yang
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Fabio Musio
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Yihui Ma
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Norman Juchler
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Johannes C Paetzold
- Department of Computing, Imperial College London, London, UK
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Rami Al-Maskari
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Luciano Höher
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA
| | | | - Anjany Sekuboyina
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Suprosanna Shit
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Houjing Huang
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Diana Waldmannstetter
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Munich, Germany
| | - Fernando Navarro
- School of Medicine, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Martin Menten
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, UK
| | - Iris Vos
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands
| | - Ynte Ruigrok
- Department of Neurology, UMC Utrecht, Utrecht, the Netherlands
| | | | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands
| | - Julien Hämmerli
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Catherine Wurster
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Philippe Bijlenga
- Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland
| | - Laura Westphal
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Jeroen Bisschop
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Elisa Colombo
- Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Hakim Baazaoui
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - James Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Bene Wiestler
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- School of Medicine, Technical University of Munich, Munich, Germany
| | - Roland Wiest
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Berne and University of Berne, Berne, Switzerland
| | - Emmanuel Montagnon
- Centre de Recherche du Centre Hospitalier de l'Université de Montreal (CRCHUM), Montreal, Canada
| | | | | | | | | | | | - Chaolong Lin
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Haoran Zhao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Zehan Zhang
- Hangzhou Genlight Medtech Co. Ltd., Hangzhou, China
| | - Sinyoung Ra
- Department of Artificial Intelligence, Sungkyunkwan University, Seoul, Korea
| | - Jongyun Hwang
- Department of Artificial Intelligence, Sungkyunkwan University, Seoul, Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Seoul, Korea
| | - Junqiang Chen
- Shanghai MediWorks Precision Instruments Co., Ltd, Shanghai, China
| | - Marek Wodzinski
- Institute of Informatics, HES-SO Valais-Wallis, Switzerland
- Department of Measurement and Electronics, AGH University of Krakow, Poland
| | - Henning Müller
- Institute of Informatics, HES-SO Valais-Wallis, Switzerland
| | - Pengcheng Shi
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Wei Liu
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Ting Ma
- Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
| | - Cansu Yalçin
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Rachika E Hamadache
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Joaquim Salvi
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Xavier Llado
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | | | - Valeriia Abramova
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Luca Giancardo
- Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics (ViCOROB), Universitat de Girona, Catalonia, Spain
| | - Jialu Liu
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Haibin Huang
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yue Cui
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Zehang Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yusheng Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Shunzhi Zhu
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Tatsat R Patel
- Canon Stroke and Vascular Research Center, Buffalo, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, Buffalo, USA
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, USA
| | | | - Huayu Wang
- Department of Radiology, University of Washington, Seattle, USA
| | | | - Chengcheng Zhu
- Department of Radiology, University of Washington, Seattle, USA
| | - Maximilian R Rokuss
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
| | - Yannick Kirchhoff
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Nico Disch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Germany
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - Julius Holzschuh
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital
| | | | - Sven Hirsch
- Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland
| | - Susanne Wegener
- Department of Neurology, University Hospital of Zurich, Zurich, Switzerland
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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7
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Fidon L, Aertsen M, Kofler F, Bink A, David AL, Deprest T, Emam D, Guffens F, Jakab A, Kasprian G, Kienast P, Melbourne A, Menze B, Mufti N, Pogledic I, Prayer D, Stuempflen M, Elslander EV, Ourselin S, Deprest J, Vercauteren T. A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation. IEEE Trans Pattern Anal Mach Intell 2024; PP:1-12. [PMID: 38198270 DOI: 10.1109/tpami.2023.3346330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities. Our code is publicly available here.
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8
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Sudre CH, Van Wijnen K, Dubost F, Adams H, Atkinson D, Barkhof F, Birhanu MA, Bron EE, Camarasa R, Chaturvedi N, Chen Y, Chen Z, Chen S, Dou Q, Evans T, Ezhov I, Gao H, Girones Sanguesa M, Gispert JD, Gomez Anson B, Hughes AD, Ikram MA, Ingala S, Jaeger HR, Kofler F, Kuijf HJ, Kutnar D, Lee M, Li B, Lorenzini L, Menze B, Molinuevo JL, Pan Y, Puybareau E, Rehwald R, Su R, Shi P, Smith L, Tillin T, Tochon G, Urien H, van der Velden BHM, van der Velpen IF, Wiestler B, Wolters FJ, Yilmaz P, de Groot M, Vernooij MW, de Bruijne M. Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021. Med Image Anal 2024; 91:103029. [PMID: 37988921 DOI: 10.1016/j.media.2023.103029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/09/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.
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Affiliation(s)
- Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Kimberlin Van Wijnen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Florian Dubost
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab Adams
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Mahlet A Birhanu
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Robin Camarasa
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - Yuan Chen
- Department of Radiology, University of Massachusetts Medical School, Worcester, USA
| | - Zihao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shuai Chen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, China
| | - Tavia Evans
- Department of Clinical Genetics and Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Ivan Ezhov
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Haojun Gao
- Department of Radiology, Zhejiang University, Hangzhou, China
| | | | - Juan Domingo Gispert
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Barcelona, Spain
| | | | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - H Rolf Jaeger
- Institute of Neurology, University College London, London, United Kingdom
| | - Florian Kofler
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Denis Kutnar
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Bo Li
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Bjoern Menze
- Department of Informatics, Technische Universitat Munchen, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Jose Luis Molinuevo
- Barcelonaß Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; H. Lundbeck A/S, Copenhagen, Denmark
| | - Yiwei Pan
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Rafael Rehwald
- Institute of Neurology, University College London, London, United Kingdom
| | - Ruisheng Su
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pengcheng Shi
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | | | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, University College London, London, United Kingdom
| | | | - Hélène Urien
- ISEP-Institut Supérieur d'Électronique de Paris, Issy-les-Moulineaux, France
| | | | - Isabelle F van der Velpen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Frank J Wolters
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Pinar Yilmaz
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; GlaxoSmithKline Research, Stevenage, United Kingdom
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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Metz MC, Ezhov I, Peeken JC, Buchner JA, Lipkova J, Kofler F, Waldmannstetter D, Delbridge C, Diehl C, Bernhardt D, Schmidt-Graf F, Gempt J, Combs SE, Zimmer C, Menze B, Wiestler B. Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model. Neurooncol Adv 2024; 6:vdad171. [PMID: 38435962 PMCID: PMC10907005 DOI: 10.1093/noajnl/vdad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
Background The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.
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Affiliation(s)
- Marie-Christin Metz
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM—Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Josef A Buchner
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
| | - Jana Lipkova
- Department of Pathology and Molecular Medicine, University of California, Irvine, Irvine, CA, USA
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- Helmholtz Artificial Intelligence Cooperation Unit, Helmholtz Zentrum Munich, Munich, Germany
- TranslaTUM—Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | | | - Claire Delbridge
- Department of Neuropathology, Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Christian Diehl
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | | | - Jens Gempt
- Department of Neurosurgery, Technical University of Munich, Munich, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
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10
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Sigmund F, Berezin O, Beliakova S, Magerl B, Drawitsch M, Piovesan A, Gonçalves F, Bodea SV, Winkler S, Bousraou Z, Grosshauser M, Samara E, Pujol-Martí J, Schädler S, So C, Irsen S, Walch A, Kofler F, Piraud M, Kornfeld J, Briggman K, Westmeyer GG. Genetically encoded barcodes for correlative volume electron microscopy. Nat Biotechnol 2023; 41:1734-1745. [PMID: 37069313 PMCID: PMC10713455 DOI: 10.1038/s41587-023-01713-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 02/14/2023] [Indexed: 04/19/2023]
Abstract
While genetically encoded reporters are common for fluorescence microscopy, equivalent multiplexable gene reporters for electron microscopy (EM) are still scarce. Here, by installing a variable number of fixation-stable metal-interacting moieties in the lumen of encapsulin nanocompartments of different sizes, we developed a suite of spherically symmetric and concentric barcodes (EMcapsulins) that are readable by standard EM techniques. Six classes of EMcapsulins could be automatically segmented and differentiated. The coding capacity was further increased by arranging several EMcapsulins into distinct patterns via a set of rigid spacers of variable length. Fluorescent EMcapsulins were expressed to monitor subcellular structures in light and EM. Neuronal expression in Drosophila and mouse brains enabled the automatic identification of genetically defined cells in EM. EMcapsulins are compatible with transmission EM, scanning EM and focused ion beam scanning EM. The expandable palette of genetically controlled EM-readable barcodes can augment anatomical EM images with multiplexed gene expression maps.
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Affiliation(s)
- Felix Sigmund
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Oleksandr Berezin
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Sofia Beliakova
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Bernhard Magerl
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Martin Drawitsch
- Research Group, Circuits of Birdsong, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Alberto Piovesan
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Filipa Gonçalves
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Silviu-Vasile Bodea
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Stefanie Winkler
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Zoe Bousraou
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Martin Grosshauser
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany
| | - Eleni Samara
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Jesús Pujol-Martí
- Department Circuits-Computation-Models, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | | | - Chun So
- Department of Meiosis, Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
| | - Stephan Irsen
- Max Planck Institute for Neurobiology of Behavior-caesar (MPINB), Bonn, Germany
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Marie Piraud
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - Joergen Kornfeld
- Research Group, Circuits of Birdsong, Max Planck Institute for Biological Intelligence, Martinsried, Germany
| | - Kevin Briggman
- Max Planck Institute for Neurobiology of Behavior-caesar (MPINB), Bonn, Germany
| | - Gil Gregor Westmeyer
- Munich Institute of Biomedical Engineering, Department of Bioscience, TUM School of Natural Sciences and TUM School of Medicine, Technical University of Munich, Munich, Germany.
- Institute for Synthetic Biomedicine, Helmholtz Munich, Neuherberg, Germany.
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11
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Buchner JA, Peeken JC, Etzel L, Ezhov I, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze BH, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Bilger A, Grosu AL, Wolff R, Kirschke JS, Eitz KA, Combs SE, Bernhardt D, Rueckert D, Piraud M, Wiestler B, Kofler F. Identifying core MRI sequences for reliable automatic brain metastasis segmentation. Radiother Oncol 2023; 188:109901. [PMID: 37678623 DOI: 10.1016/j.radonc.2023.109901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/27/2023] [Accepted: 09/01/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation. METHODS We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers. A baseline 3D U-Net with all four sequences and six U-Nets with plausible sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE + T2-FLAIR, T1-CE + T1 + T2-FLAIR, T1-CE + T1) were trained on 239 patients from two centers and subsequently tested on an external cohort of 100 patients from five centers. RESULTS The model based on T1-CE alone achieved the best segmentation performance for BM segmentation with a median Dice similarity coefficient (DSC) of 0.96. Models trained without T1-CE performed worse (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For edema segmentation, models that included both T1-CE and T2-FLAIR performed best (DSC = 0.93), while the remaining four models without simultaneous inclusion of these both sequences reached a median DSC of 0.81-0.89. CONCLUSIONS A T1-CE-only protocol suffices for the segmentation of BMs. The combination of T1-CE and T2-FLAIR is important for edema segmentation. Missing either T1-CE or T2-FLAIR decreases performance. These findings may improve imaging routines by omitting unnecessary sequences, thus allowing for faster procedures in daily clinical practice while enabling optimal neural network-based target definitions.
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Affiliation(s)
- Josef A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Sebastian M Christ
- Department of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Thomas B Brunner
- Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - Andrea Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - Bjoern H Menze
- Department of Informatics, Technical University of Munich, Munich, Germany; Department of Quantitative Biomedicine, University Hospital and University of Zurich, Zurich, Switzerland
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Rami A El Shafie
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
| | - Susanne Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Katrin Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Horst J Feldmann
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig Holstein, Kiel, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Angelika Bilger
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Anca L Grosu
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Robert Wolff
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Guestrow, Germany; Department of Neurosurgery, University Hospital Frankfurt, Frankfurt, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Kerstin A Eitz
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Marie Piraud
- Helmholtz AI, Helmholtz Zentrum Munich, Neuherberg, Germany
| | - Benedikt Wiestler
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Helmholtz AI, Helmholtz Zentrum Munich, Neuherberg, Germany; Department of Informatics, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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12
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Buchner JA, Kofler F, Mayinger MC, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, Shafie RE, Rogers S, Schulze K, Blanck O, Zamboglou C, Grosu A, Combs SE, Bernhardt D, Wiestler B, Peeken JC. What MRI Sequences are Necessary for Automated Neural Network-Based Metastasis Segmentation - An Ablation Study. Int J Radiat Oncol Biol Phys 2023; 117:e704-e705. [PMID: 37786065 DOI: 10.1016/j.ijrobp.2023.06.2195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Brain metastasis (BM) delineation is a time-consuming process in both daily clinical practice and research. Automated BM segmentation algorithms can be used to assist in this task. Most approaches to brain tumor segmentation, such as algorithms trained on the BraTS challenge, use four magnetic resonance imaging (MRI) sequences as input, making them susceptible to missing or corrupted sequences and increase the number of sequences necessary for MRI RT planning. The goal of this project is to compare neural networks with different combinations of input sequences for the segmentation of the contrast-enhancing metastasis and the surrounding FLAIR hyperintense edema. All models were tested in a multicenter international external test cohort. This allows us to determine which MRI sequences are needed for effective automated segmentations. MATERIALS/METHODS In total, we had T1-weighted sequences without (T1) and with contrast enhancement (T1-CE), T2-weighted sequences (T2), and T2 fluid-attenuated inversion recovery (FLAIR) sequences from 339 patients with at least one brain metastasis from seven centers available. Preprocessing yielded co-registered, skull-stripped sequences with an isotropic resolution of 1 millimeter. The contrast-enhancing metastasis as well as the surrounding FLAIR hyperintense edema were manually segmented to create reference labels. A baseline 3D U-Net with all four sequences as well as six additional U-Nets with different clinically plausible combinations (T1-CE; T1; FLAIR; T1-CE+FLAIR; T1-CE+T1+FLAIR; T1-CE+T1) of input sequences were trained on a cohort of 239 patients from two centers and subsequently tested on an external cohort of 100 patients from the remaining five centers. RESULTS All models that included T1-CE in their selected sequences showed similar performance for metastasis segmentation with a median Dice similarity coefficient (DSC) of 0.93-0.96. T1-CE alone likewise achieved a performance of 0.96 (IQR 0.93-0.97). The model trained with only FLAIR performed worse (DSC = 0.73, IQR 0.54-0.84). For edema segmentation, models that included both T1-CE and FLAIR performed best (median DSC = 0.93), while the remaining four models without simultaneous inclusion of these two sequences (T1-CE; T1; FLAIR; T1-CE+T1) reached a median DSC of 0.81-0.89. CONCLUSION Automatic segmentation of brain metastases with less than four input sequences is feasible with minimal or no loss of quality. A T1-CE-only protocol suffices for metastasis segmentation. In contrast, for edema segmentation, the combination of T1-CE and FLAIR seems to be important. Missing either T1-CE or FLAIR decreases performance. These findings may improve future imaging routines by omitting unnecessary sequences, thus speeding up procedures in daily clinical practice while allowing for optimal neural network-based target definitions.
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Affiliation(s)
- J A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - F Kofler
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany; Department of Informatics, Technical University of Munich, Munich, Germany
| | - M C Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - T B Brunner
- Medical University of Graz, Dept. of Radiation Oncology, Graz, Austria; Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - A Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - B Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - C Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - B Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - M Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - N Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - R El Shafie
- Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - S Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - K Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - O Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - C Zamboglou
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus; Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany
| | - A Grosu
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - S E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - D Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), partner site Munich, Munich, Germany
| | - B Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - J C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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Schlaeger S, Drummer K, El Husseini M, Kofler F, Sollmann N, Schramm S, Zimmer C, Wiestler B, Kirschke JS. Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset. Eur Radiol 2023; 33:5882-5893. [PMID: 36928566 PMCID: PMC10326102 DOI: 10.1007/s00330-023-09512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/17/2022] [Accepted: 02/03/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVES T2-weighted (w) fat sat (fs) sequences, which are important in spine MRI, require a significant amount of scan time. Generative adversarial networks (GANs) can generate synthetic T2-w fs images. We evaluated the potential of synthetic T2-w fs images by comparing them to their true counterpart regarding image and fat saturation quality, and diagnostic agreement in a heterogenous, multicenter dataset. METHODS A GAN was used to synthesize T2-w fs from T1- and non-fs T2-w. The training dataset comprised scans of 73 patients from two scanners, and the test dataset, scans of 101 patients from 38 multicenter scanners. Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured in true and synthetic T2-w fs. Two neuroradiologists graded image (5-point scale) and fat saturation quality (3-point scale). To evaluate whether the T2-w fs images are indistinguishable, a Turing test was performed by eleven neuroradiologists. Six pathologies were graded on the synthetic protocol (with synthetic T2-w fs) and the original protocol (with true T2-w fs) by the two neuroradiologists. RESULTS aSNR and aCNR were not significantly different between the synthetic and true T2-w fs images. Subjective image quality was graded higher for synthetic T2-w fs (p = 0.023). In the Turing test, synthetic and true T2-w fs could not be distinguished from each other. The intermethod agreement between synthetic and original protocol ranged from substantial to almost perfect agreement for the evaluated pathologies. DISCUSSION The synthetic T2-w fs might replace a physical T2-w fs. Our approach validated on a challenging, multicenter dataset is highly generalizable and allows for shorter scan protocols. KEY POINTS • Generative adversarial networks can be used to generate synthetic T2-weighted fat sat images from T1- and non-fat sat T2-weighted images of the spine. • The synthetic T2-weighted fat sat images might replace a physically acquired T2-weighted fat sat showing a better image quality and excellent diagnostic agreement with the true T2-weighted fat images. • The present approach validated on a challenging, multicenter dataset is highly generalizable and allows for significantly shorter scan protocols.
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Affiliation(s)
- Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Katharina Drummer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Malek El Husseini
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Zentrum München, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-NeuroImaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-NeuroImaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-NeuroImaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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14
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Mai H, Luo J, Hoeher L, Al-Maskari R, Horvath I, Chen Y, Kofler F, Piraud M, Paetzold JC, Modamio J, Todorov M, Elsner M, Hellal F, Ertürk A. Whole-body cellular mapping in mouse using standard IgG antibodies. Nat Biotechnol 2023:10.1038/s41587-023-01846-0. [PMID: 37430076 DOI: 10.1038/s41587-023-01846-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 05/26/2023] [Indexed: 07/12/2023]
Abstract
Whole-body imaging techniques play a vital role in exploring the interplay of physiological systems in maintaining health and driving disease. We introduce wildDISCO, a new approach for whole-body immunolabeling, optical clearing and imaging in mice, circumventing the need for transgenic reporter animals or nanobody labeling and so overcoming existing technical limitations. We identified heptakis(2,6-di-O-methyl)-β-cyclodextrin as a potent enhancer of cholesterol extraction and membrane permeabilization, enabling deep, homogeneous penetration of standard antibodies without aggregation. WildDISCO facilitates imaging of peripheral nervous systems, lymphatic vessels and immune cells in whole mice at cellular resolution by labeling diverse endogenous proteins. Additionally, we examined rare proliferating cells and the effects of biological perturbations, as demonstrated in germ-free mice. We applied wildDISCO to map tertiary lymphoid structures in the context of breast cancer, considering both primary tumor and metastases throughout the mouse body. An atlas of high-resolution images showcasing mouse nervous, lymphatic and vascular systems is accessible at http://discotechnologies.org/wildDISCO/atlas/index.php .
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Affiliation(s)
- Hongcheng Mai
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany
- Munich Medical Research School, Munich, Germany
- Deep Piction GmbH, Munich, Germany
| | - Jie Luo
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany
- Deep Piction GmbH, Munich, Germany
| | - Luciano Hoeher
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
| | - Rami Al-Maskari
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Izabela Horvath
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Ying Chen
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany
- Faculty of Medicine, Ludwig-Maximilians University of Munich, Munich, Germany
| | - Florian Kofler
- Helmholtz Al, Helmholtz Center Munich, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Piraud
- Helmholtz Al, Helmholtz Center Munich, Neuherberg, Germany
| | - Johannes C Paetzold
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Department of Computing, Imperial College London, London, UK
| | - Jennifer Modamio
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
| | - Mihail Todorov
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Markus Elsner
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
| | - Farida Hellal
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Center Munich, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Medical Centre of the University of Munich, Ludwig-Maximilians University of Munich, Munich, Germany.
- Deep Piction GmbH, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
- Graduate School of Neuroscience (GSN), Munich, Germany.
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15
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Li HB, Conte GM, Anwar SM, Kofler F, Ezhov I, van Leemput K, Piraud M, Diaz M, Cole B, Calabrese E, Rudie J, Meissen F, Adewole M, Janas A, Kazerooni AF, LaBella D, Moawad AW, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Johanson E, Meier Z, Davatzikos C, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh HM, Wiest R, Kirschke J, Colen RR, Kotrotsou A, Lamontagne P, Marcus D, Milchenko M, Nazeri A, Weber MA, Mahajan A, Mohan S, Mongan J, Hess C, Cha S, Villanueva-Meyer J, Colak E, Crivellaro P, Jakab A, Albrecht J, Anazodo U, Aboian M, Yu T, Chung V, Bergquist T, Eddy J, Albrecht J, Baid U, Bakas S, Linguraru MG, Menze B, Iglesias JE, Wiestler B. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn). ArXiv 2023:arXiv:2305.09011v5. [PMID: 37608932 PMCID: PMC10441440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.
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Affiliation(s)
- Hongwei Bran Li
- University of Zurich, Switzerland
- Department of Informatics, Technical University Munich, Germany
- Klinikum rechts der Isar, Technical University of Munich, Germany
| | | | - Syed Muhammad Anwar
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Florian Kofler
- Helmholtz AI, Helmholtz Munich, Germany
- Department of Informatics, Technical University Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | | | | | | | | | - Evan Calabrese
- Duke University Medical Center, Department of Radiology, USA
- University of California San Francisco, CA, USA
| | - Jeff Rudie
- University of California San Francisco, CA, USA
| | - Felix Meissen
- Department of Informatics, Technical University Munich, Germany
| | - Maruf Adewole
- Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria
| | | | - Anahita Fathi Kazerooni
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Dominic LaBella
- Duke University Medical Center, Department of Radiation Oncology, USA
| | | | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | | | | | | | - Russell Takeshi Shinohara
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Walter Wiggins
- Duke University Medical Center, Department of Radiology, USA
| | - Zachary Reitman
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Chunhao Wang
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Xinyang Liu
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Zhifan Jiang
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Ariana Familiar
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Christos Davatzikos
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Freymann
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Justin Kirby
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21701, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - Michel Bilello
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Roland Wiest
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
- Support Centre for Advanced Neuroimaging Inselspital, Institute for Diagnostic and Interventional Neuroradiology, Bern University Hospital, Bern, Switzerland
| | - Jan Kirschke
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Rivka R Colen
- University of Pittsburgh Medical Center, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Daniel Marcus
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Mikhail Milchenko
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Arash Nazeri
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Ernst-Heydemann-Str. 6, 18057 Rostock, Germany
| | - Abhishek Mahajan
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Suyash Mohan
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Mongan
- University of California San Francisco, CA, USA
| | | | - Soonmee Cha
- University of California San Francisco, CA, USA
| | | | | | | | | | | | - Udunna Anazodo
- Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | | | - Thomas Yu
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, USA
| | | | | | | | | | - Ujjwal Baid
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for AI and Data Science for Integrated Diagnostics (AI2D) I& Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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16
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Moawad AW, Janas A, Baid U, Ramakrishnan D, Jekel L, Krantchev K, Moy H, Saluja R, Osenberg K, Wilms K, Kaur M, Avesta A, Pedersen GC, Maleki N, Salimi M, Merkaj S, von Reppert M, Tillmans N, Lost J, Bousabarah K, Holler W, Lin M, Westerhoff M, Maresca R, Link KE, Tahon NH, Marcus D, Sotiras A, LaMontagne P, Chakrabarty S, Teytelboym O, Youssef A, Nada A, Velichko YS, Gennaro N, Cramer J, Johnson DR, Kwan BY, Petrovic B, Patro SN, Wu L, So T, Thompson G, Kam A, Perez-Carrillo GG, Lall N, Albrecht J, Anazodo U, Lingaru MG, Menze BH, Wiestler B, Adewole M, Anwar SM, Labella D, Li HB, Iglesias JE, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Dako F, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Van Leemput K, Piraud M, Ezhov I, Johanson E, Meier Z, Familiar A, Kazerooni AF, Kofler F, Calabrese E, Aneja S, Chiang V, Ikuta I, Shafique U, Memon F, Conte GM, Bakas S, Rudie J, Aboian M. The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI. ArXiv 2023:arXiv:2306.00838v1. [PMID: 37396600 PMCID: PMC10312806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.
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Affiliation(s)
| | - Anastasia Janas
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Charité - Universitatsmedizin, Berlin, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania School of Medicine, Philadelphia, PA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Divya Ramakrishnan
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Leon Jekel
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- DKFZ Division of Translational Neurooncology at the WTZ, German Cancer Consortium, DKTK Partner Site, University Hospital Essen, Essen, Germany
- German Cancer Research Center, Heidelberg, Germany
- University of Ulm, Ulm, Germany
| | - Kiril Krantchev
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Charité - Universitatsmedizin, Berlin, Germany
| | - Harrison Moy
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Klara Osenberg
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Klara Wilms
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Manpreet Kaur
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- Ludwig Maximillian University, Munich, Germany
| | - Arman Avesta
- Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Gabriel Cassinelli Pedersen
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Nazanin Maleki
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Mahdi Salimi
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | - Sarah Merkaj
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Ulm, Ulm, Germany
| | - Marc von Reppert
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Leipzig, Leipzig, Germany
| | - Niklas Tillmans
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | - Jan Lost
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
- University of Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany
| | | | | | - MingDe Lin
- Visage Imaging, Inc, San Diego, California, USA
| | | | - Ryan Maresca
- Yale University School of Medicine, Department of Therapeutic Radiology, New Haven, CT
| | | | | | | | | | | | | | | | - Ayda Youssef
- Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Yuri S. Velichko
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL
| | - Nicolo Gennaro
- Northwestern University, Department of Radiology, Feinberg School of Medicine, Chicago, IL
| | - Connectome Students
- Connectome – Student Association for Neurosurgery, Neurology and Neurosciences E.V
| | | | | | | | - Benjamin Y.M. Kwan
- Queen’s University, Department of Diagnostic Radiology, Kingston, Canada
| | | | - Satya N. Patro
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Lei Wu
- University of Washington Department of Radiology, Seattle, WA
| | - Tiffany So
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong
| | | | - Anthony Kam
- Loyola University Medical Center, Chicago, IL
| | | | - Neil Lall
- Children’s Healthcare of Atlanta, Atlanta, GA
| | - Group of Approvers
- Connectome – Student Association for Neurosurgery, Neurology and Neurosciences E.V
| | | | - Udunna Anazodo
- Montreal Neurological Institute (MNI), McGill University, Montreal, CA
| | | | - Bjoern H Menze
- Biomedical Image Analysis & Machine Learning, Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Maruf Adewole
- Medical Artificial Intelligence (MAI) Lab, Crestview Radiology, Lagos, Nigeria
| | | | | | - Hongwei Bran Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | | | | | | | - Russel Takeshi Shinohara
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA
| | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Xinyang Liu
- Children’s National Hospital, Washington DC, USA
| | - Zhifan Jiang
- Children’s National Hospital, Washington DC, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | | | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Germany
| | - Elaine Johanson
- PrecisionFDA, U.S. Food and Drug Administration, Silver Spring, MD
| | | | - Ariana Familiar
- Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Sanjay Aneja
- Yale University School of Medicine, Department of Therapeutic Radiology, New Haven, CT
| | - Veronica Chiang
- Yale University School of Medicine, Department of Neurosurgery, New Haven, CT
| | | | | | - Fatima Memon
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania School of Medicine, Philadelphia, PA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Rudie
- University of California San Diego, San Diego, CA
- University of California San Francisco, San Francisco, CA
| | - Mariam Aboian
- Yale University School of Medicine, Department of Radiology, New Haven, CT
- ImagineQuant, Yale University School of Medicine, Department of Radiology, New Haven, CT
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Adewole M, Rudie JD, Gbdamosi A, Toyobo O, Raymond C, Zhang D, Omidiji O, Akinola R, Suwaid MA, Emegoakor A, Ojo N, Aguh K, Kalaiwo C, Babatunde G, Ogunleye A, Gbadamosi Y, Iorpagher K, Calabrese E, Aboian M, Linguraru M, Albrecht J, Wiestler B, Kofler F, Janas A, LaBella D, Kzerooni AF, Li HB, Iglesias JE, Farahani K, Eddy J, Bergquist T, Chung V, Shinohara RT, Wiggins W, Reitman Z, Wang C, Liu X, Jiang Z, Familiar A, Van Leemput K, Bukas C, Piraud M, Conte GM, Johansson E, Meier Z, Menze BH, Baid U, Bakas S, Dako F, Fatade A, Anazodo UC. The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa). ArXiv 2023:arXiv:2305.19369v1. [PMID: 37396608 PMCID: PMC10312814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.
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Affiliation(s)
- Maruf Adewole
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Department of Radiation Biology, Radiotherapy and Radiodiagnosis, University of Lagos, Lagos, Nigeria
| | - Jeffrey D Rudie
- Department of Radiology, University of California, San Diego
| | - Anu Gbdamosi
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Crestview Radiology Limited, Lagos, Nigeria
| | - Oluyemisi Toyobo
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Crestview Radiology Limited, Lagos, Nigeria
| | | | - Dong Zhang
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
| | - Olubukola Omidiji
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Lagos University Teaching Hospital, Lagos, Nigeria
| | - Rachel Akinola
- Lagos State University Teaching Hospital, Ikeja, Lagos, Nigeria
| | | | - Adaobi Emegoakor
- Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria
| | - Nancy Ojo
- Federal Medical Centre, Abeokuta, Ogun State, Nigeria
| | - Kenneth Aguh
- Federal Medical Centre, Umahia, Abia State, Nigeria
| | | | | | | | | | - Kator Iorpagher
- Benue State University Teaching Hospital, Markurdi, Benue State, Nigeria
| | - Evan Calabrese
- Duke University Medical Center, Department of Radiology, USA
- University of California San Francisco, CA, USA
| | | | - Marius Linguraru
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | | | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
- Helmholtz Research Center, Munich, Germany
| | | | - Dominic LaBella
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Anahita Fathi Kzerooni
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Center for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Hongwei Bran Li
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- University of Zurich, Switzerland
| | - Juan Eugenio Iglesias
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | | | | | | | - Russell Takeshi Shinohara
- Center for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA
| | - Walter Wiggins
- Duke University Medical Center, Department of Radiology, USA
| | - Zachary Reitman
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Chunhao Wang
- Duke University Medical Center, Department of Radiation Oncology, USA
| | - Xinyang Liu
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Zhifan Jiang
- Children's National Hospital, Washington DC, USA
- George Washington University, Washington DC, USA
| | - Ariana Familiar
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | | | | | | | - Elaine Johansson
- Precision FDA, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Bjoern H Menze
- Department of Neuroradiology, Technical University of Munich, Munich, Germany
- University of Zurich, Switzerland
| | - Ujjwal Baid
- Center for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Spyridon Bakas
- Center for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Farouk Dako
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Abiodun Fatade
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Crestview Radiology Limited, Lagos, Nigeria
| | - Udunna C Anazodo
- Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Department of Medicine, University of Cape Town, South Africa
- Department of Radiation Medicine, University of Cape Town, South Africa
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18
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LaBella D, Adewole M, Alonso-Basanta M, Altes T, Anwar SM, Baid U, Bergquist T, Bhalerao R, Chen S, Chung V, Conte GM, Dako F, Eddy J, Ezhov I, Godfrey D, Hilal F, Familiar A, Farahani K, Iglesias JE, Jiang Z, Johanson E, Kazerooni AF, Kent C, Kirkpatrick J, Kofler F, Leemput KV, Li HB, Liu X, Mahtabfar A, McBurney-Lin S, McLean R, Meier Z, Moawad AW, Mongan J, Nedelec P, Pajot M, Piraud M, Rashid A, Reitman Z, Shinohara RT, Velichko Y, Wang C, Warman P, Wiggins W, Aboian M, Albrecht J, Anazodo U, Bakas S, Flanders A, Janas A, Khanna G, Linguraru MG, Menze B, Nada A, Rauschecker AM, Rudie J, Tahon NH, Villanueva-Meyer J, Wiestler B, Calabrese E. The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma. ArXiv 2023:arXiv:2305.07642v1. [PMID: 37608937 PMCID: PMC10441446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.
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19
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Schlaeger S, Drummer K, Husseini ME, Kofler F, Sollmann N, Schramm S, Zimmer C, Kirschke JS, Wiestler B. Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection. Diagnostics (Basel) 2023; 13:diagnostics13050974. [PMID: 36900118 PMCID: PMC10000723 DOI: 10.3390/diagnostics13050974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/16/2023] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
(1) Background and Purpose: In magnetic resonance imaging (MRI) of the spine, T2-weighted (T2-w) fat-saturated (fs) images improve the diagnostic assessment of pathologies. However, in the daily clinical setting, additional T2-w fs images are frequently missing due to time constraints or motion artifacts. Generative adversarial networks (GANs) can generate synthetic T2-w fs images in a clinically feasible time. Therefore, by simulating the radiological workflow with a heterogenous dataset, this study's purpose was to evaluate the diagnostic value of additional synthetic, GAN-based T2-w fs images in the clinical routine. (2) Methods: 174 patients with MRI of the spine were retrospectively identified. A GAN was trained to synthesize T2-w fs images from T1-w, and non-fs T2-w images of 73 patients scanned in our institution. Subsequently, the GAN was used to create synthetic T2-w fs images for the previously unseen 101 patients from multiple institutions. In this test dataset, the additional diagnostic value of synthetic T2-w fs images was assessed in six pathologies by two neuroradiologists. Pathologies were first graded on T1-w and non-fs T2-w images only, then synthetic T2-w fs images were added, and pathologies were graded again. Evaluation of the additional diagnostic value of the synthetic protocol was performed by calculation of Cohen's ĸ and accuracy in comparison to a ground truth (GT) grading based on real T2-w fs images, pre- or follow-up scans, other imaging modalities, and clinical information. (3) Results: The addition of the synthetic T2-w fs to the imaging protocol led to a more precise grading of abnormalities than when grading was based on T1-w and non-fs T2-w images only (mean ĸ GT versus synthetic protocol = 0.65; mean ĸ GT versus T1/T2 = 0.56; p = 0.043). (4) Conclusions: The implementation of synthetic T2-w fs images in the radiological workflow significantly improves the overall assessment of spine pathologies. Thereby, high-quality, synthetic T2-w fs images can be virtually generated by a GAN from heterogeneous, multicenter T1-w and non-fs T2-w contrasts in a clinically feasible time, which underlines the reproducibility and generalizability of our approach.
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Affiliation(s)
- Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- Correspondence:
| | - Katharina Drummer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Malek El Husseini
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
- TranslaTUM—Central Institute for Translational Cancer Research, Technical University of Munich, Einsteinstr. 25, 81675 Munich, Germany
- Helmholtz AI, Helmholtz Zentrum München, Ingostaedter Landstrasse 1, 85764 Oberschleissheim, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- TUM-NeuroImaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- TUM-NeuroImaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
- TUM-NeuroImaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
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20
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Zhylka A, Sollmann N, Kofler F, Radwan A, De Luca A, Gempt J, Wiestler B, Menze B, Schroeder A, Zimmer C, Kirschke JS, Sunaert S, Leemans A, Krieg SM, Pluim J. Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping. AJNR Am J Neuroradiol 2023; 44:283-290. [PMID: 36797033 PMCID: PMC10187805 DOI: 10.3174/ajnr.a7793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 01/17/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND AND PURPOSE Tractography of the corticospinal tract is paramount to presurgical planning and guidance of intraoperative resection in patients with motor-eloquent gliomas. It is well-known that DTI-based tractography as the most frequently used technique has relevant shortcomings, particularly for resolving complex fiber architecture. The purpose of this study was to evaluate multilevel fiber tractography combined with functional motor cortex mapping in comparison with conventional deterministic tractography algorithms. MATERIALS AND METHODS Thirty-one patients (mean age, 61.5 [SD, 12.2] years) with motor-eloquent high-grade gliomas underwent MR imaging with DWI (TR/TE = 5000/78 ms, voxel size = 2 × 2 × 2 mm3, 1 volume at b = 0 s/mm2, 32 volumes at b = 1000 s/mm2). DTI, constrained spherical deconvolution, and multilevel fiber tractography-based reconstruction of the corticospinal tract within the tumor-affected hemispheres were performed. The functional motor cortex was enclosed by navigated transcranial magnetic stimulation motor mapping before tumor resection and used for seeding. A range of angular deviation and fractional anisotropy thresholds (for DTI) was tested. RESULTS For all investigated thresholds, multilevel fiber tractography achieved the highest mean coverage of the motor maps (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 71.8%, 22.6%, and 11.7%) and the most extensive corticospinal tract reconstructions (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 26,485 mm3, 6308 mm3, and 4270 mm3). CONCLUSIONS Multilevel fiber tractography may improve the coverage of the motor cortex by corticospinal tract fibers compared with conventional deterministic algorithms. Thus, it could provide a more detailed and complete visualization of corticospinal tract architecture, particularly by visualizing fiber trajectories with acute angles that might be of high relevance in patients with gliomas and distorted anatomy.
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Affiliation(s)
- A Zhylka
- From the Department of Biomedical Engineering (A.Z., J.P.), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - N Sollmann
- Department of Diagnostic and Interventional Radiology (N.S.), University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology (N.S., F.K., B.W., C.Z., J.S.K.), School of Medicine, Klinikum rechts der Isar
- TUM-Neuroimaging Center (N.S., C.Z., J.S.K., S.M.K.), Klinikum rechts der Isar
- Department of Radiology and Biomedical Imaging (N.S.), University of California, San Francisco, San Francisco, California
| | - F Kofler
- Helmholtz AI (F.K.), Helmholtz Zentrum Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology (N.S., F.K., B.W., C.Z., J.S.K.), School of Medicine, Klinikum rechts der Isar
- Image-Based Biomedical Modeling (F.K., B.M.)
- Department of Informatics, TranslaTUM (F.K., B.W.), Central Institute for Translational Cancer Research
| | - A Radwan
- Department of Imaging and Pathology (A.R., S.S.), Translational MRI
- Department of Neurosciences (A.R., S.S.), Leuven Brain Institute, Katholieke Universiteit Leuven, Leuven, Belgium
| | - A De Luca
- Image Sciences Institute (A.D.L., A.L.)
- Neurology Department (A.D.L.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J Gempt
- Department of Neurosurgery (J.G., A.S., S.M.K.), School of Medicine, Klinikumrechts der Isar, Technical University of Munich, Munich, Germany
| | - B Wiestler
- Department of Diagnostic and Interventional Neuroradiology (N.S., F.K., B.W., C.Z., J.S.K.), School of Medicine, Klinikum rechts der Isar
- Department of Informatics, TranslaTUM (F.K., B.W.), Central Institute for Translational Cancer Research
| | - B Menze
- Image-Based Biomedical Modeling (F.K., B.M.)
- Department of Quantitative Biomedicine (B.M.), University of Zurich, Zurich, Switzerland
| | - A Schroeder
- Department of Neurosurgery (J.G., A.S., S.M.K.), School of Medicine, Klinikumrechts der Isar, Technical University of Munich, Munich, Germany
| | - C Zimmer
- Department of Diagnostic and Interventional Neuroradiology (N.S., F.K., B.W., C.Z., J.S.K.), School of Medicine, Klinikum rechts der Isar
- TUM-Neuroimaging Center (N.S., C.Z., J.S.K., S.M.K.), Klinikum rechts der Isar
| | - J S Kirschke
- Department of Diagnostic and Interventional Neuroradiology (N.S., F.K., B.W., C.Z., J.S.K.), School of Medicine, Klinikum rechts der Isar
- TUM-Neuroimaging Center (N.S., C.Z., J.S.K., S.M.K.), Klinikum rechts der Isar
| | - S Sunaert
- Department of Imaging and Pathology (A.R., S.S.), Translational MRI
- Department of Neurosciences (A.R., S.S.), Leuven Brain Institute, Katholieke Universiteit Leuven, Leuven, Belgium
| | - A Leemans
- Image Sciences Institute (A.D.L., A.L.)
| | - S M Krieg
- TUM-Neuroimaging Center (N.S., C.Z., J.S.K., S.M.K.), Klinikum rechts der Isar
- Department of Neurosurgery (J.G., A.S., S.M.K.), School of Medicine, Klinikumrechts der Isar, Technical University of Munich, Munich, Germany
| | - J Pluim
- From the Department of Biomedical Engineering (A.Z., J.P.), Eindhoven University of Technology, Eindhoven, The Netherlands
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21
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Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, Szeskin A, Jacobs C, Mamani GEH, Chartrand G, Lohöfer F, Holch JW, Sommer W, Hofmann F, Hostettler A, Lev-Cohain N, Drozdzal M, Amitai MM, Vivanti R, Sosna J, Ezhov I, Sekuboyina A, Navarro F, Kofler F, Paetzold JC, Shit S, Hu X, Lipková J, Rempfler M, Piraud M, Kirschke J, Wiestler B, Zhang Z, Hülsemeyer C, Beetz M, Ettlinger F, Antonelli M, Bae W, Bellver M, Bi L, Chen H, Chlebus G, Dam EB, Dou Q, Fu CW, Georgescu B, Giró-I-Nieto X, Gruen F, Han X, Heng PA, Hesser J, Moltz JH, Igel C, Isensee F, Jäger P, Jia F, Kaluva KC, Khened M, Kim I, Kim JH, Kim S, Kohl S, Konopczynski T, Kori A, Krishnamurthi G, Li F, Li H, Li J, Li X, Lowengrub J, Ma J, Maier-Hein K, Maninis KK, Meine H, Merhof D, Pai A, Perslev M, Petersen J, Pont-Tuset J, Qi J, Qi X, Rippel O, Roth K, Sarasua I, Schenk A, Shen Z, Torres J, Wachinger C, Wang C, Weninger L, Wu J, Xu D, Yang X, Yu SCH, Yuan Y, Yue M, Zhang L, Cardoso J, Bakas S, Braren R, Heinemann V, Pal C, Tang A, Kadoury S, Soler L, van Ginneken B, Greenspan H, Joskowicz L, Menze B. The Liver Tumor Segmentation Benchmark (LiTS). Med Image Anal 2023; 84:102680. [PMID: 36481607 PMCID: PMC10631490 DOI: 10.1016/j.media.2022.102680] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 09/27/2022] [Accepted: 10/29/2022] [Indexed: 11/18/2022]
Abstract
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.
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Affiliation(s)
- Patrick Bilic
- Department of Informatics, Technical University of Munich, Germany
| | - Patrick Christ
- Department of Informatics, Technical University of Munich, Germany
| | - Hongwei Bran Li
- Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland.
| | | | - Avi Ben-Cohen
- Department of Biomedical Engineering, Tel-Aviv University, Israel
| | - Georgios Kaissis
- Institute for AI in Medicine, Technical University of Munich, Germany; Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Adi Szeskin
- School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel
| | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Gabriel Chartrand
- The University of Montréal Hospital Research Centre (CRCHUM) Montréal, Québec, Canada
| | - Fabian Lohöfer
- Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany
| | - Julian Walter Holch
- Department of Medicine III, University Hospital, LMU Munich, Munich, Germany; Comprehensive Cancer Center Munich, Munich, Germany; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wieland Sommer
- Department of Radiology, University Hospital, LMU Munich, Germany
| | - Felix Hofmann
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Germany; Department of Radiology, University Hospital, LMU Munich, Germany
| | - Alexandre Hostettler
- Department of Surgical Data Science, Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD), France
| | - Naama Lev-Cohain
- Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel
| | | | | | | | - Jacob Sosna
- Department of Radiology, Hadassah University Medical Center, Jerusalem, Israel
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Germany
| | - Anjany Sekuboyina
- Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Fernando Navarro
- Department of Informatics, Technical University of Munich, Germany; Department of Radiation Oncology and Radiotherapy, Klinikum rechts der Isar, Technical University of Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Florian Kofler
- Department of Informatics, Technical University of Munich, Germany; Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany; Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
| | - Johannes C Paetzold
- Department of Computing, Imperial College London, London, United Kingdom; Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany
| | - Suprosanna Shit
- Department of Informatics, Technical University of Munich, Germany
| | - Xiaobin Hu
- Department of Informatics, Technical University of Munich, Germany
| | - Jana Lipková
- Brigham and Women's Hospital, Harvard Medical School, USA
| | - Markus Rempfler
- Department of Informatics, Technical University of Munich, Germany
| | - Marie Piraud
- Department of Informatics, Technical University of Munich, Germany; Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jan Kirschke
- Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany
| | - Benedikt Wiestler
- Institute for diagnostic and interventional neuroradiology, Klinikum rechts der Isar,Technical University of Munich, Germany
| | - Zhiheng Zhang
- Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School, China
| | | | - Marcel Beetz
- Department of Informatics, Technical University of Munich, Germany
| | | | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | | | | | - Lei Bi
- School of Computer Science, the University of Sydney, Australia
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China
| | - Grzegorz Chlebus
- Fraunhofer MEVIS, Bremen, Germany; Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Erik B Dam
- Department of Computer Science, University of Copenhagen, Denmark
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Chi-Wing Fu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | | | - Xavier Giró-I-Nieto
- Signal Theory and Communications Department, Universitat Politecnica de Catalunya, Catalonia, Spain
| | - Felix Gruen
- Institute of Control Engineering, Technische Universität Braunschweig, Germany
| | - Xu Han
- Department of computer science, UNC Chapel Hill, USA
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Jürgen Hesser
- Mannheim Institute for Intelligent Systems in Medicine, department of Medicine Mannheim, Heidelberg University, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany; Central Institute for Computer Engineering (ZITI), Heidelberg University, Germany
| | | | - Christian Igel
- Department of Computer Science, University of Copenhagen, Denmark
| | - Fabian Isensee
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, Germany
| | - Paul Jäger
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, Germany
| | - Fucang Jia
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Krishna Chaitanya Kaluva
- Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India
| | - Mahendra Khened
- Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India
| | | | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, South Korea
| | | | - Simon Kohl
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tomasz Konopczynski
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany
| | - Avinash Kori
- Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India
| | - Ganapathy Krishnamurthi
- Medical Imaging and Reconstruction Lab, Department of Engineering Design, Indian Institute of Technology Madras, India
| | - Fan Li
- Sensetime, Shanghai, China
| | - Hongchao Li
- Department of Computer Science, Guangdong University of Foreign Studies, China
| | - Junbo Li
- Philips Research China, Philips China Innovation Campus, Shanghai, China
| | - Xiaomeng Li
- Department of Electrical and Electronic Engineering, The University of Hong Kong, China
| | - John Lowengrub
- Departments of Mathematics, Biomedical Engineering, University of California, Irvine, USA; Center for Complex Biological Systems, University of California, Irvine, USA; Chao Family Comprehensive Cancer Center, University of California, Irvine, USA
| | - Jun Ma
- Department of Mathematics, Nanjing University of Science and Technology, China
| | - Klaus Maier-Hein
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, Germany
| | | | - Hans Meine
- Fraunhofer MEVIS, Bremen, Germany; Medical Image Computing Group, FB3, University of Bremen, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, Germany
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, Denmark
| | - Mathias Perslev
- Department of Computer Science, University of Copenhagen, Denmark
| | - Jens Petersen
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jordi Pont-Tuset
- Eidgenössische Technische Hochschule Zurich (ETHZ), Zurich, Switzerland
| | - Jin Qi
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, China
| | - Xiaojuan Qi
- Department of Electrical and Electronic Engineering, The University of Hong Kong, China
| | - Oliver Rippel
- Institute of Imaging & Computer Vision, RWTH Aachen University, Germany
| | | | - Ignacio Sarasua
- Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Andrea Schenk
- Fraunhofer MEVIS, Bremen, Germany; Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Zengming Shen
- Beckman Institute, University of Illinois at Urbana-Champaign, USA; Siemens Healthineers, USA
| | - Jordi Torres
- Barcelona Supercomputing Center, Barcelona, Spain; Universitat Politecnica de Catalunya, Catalonia, Spain
| | - Christian Wachinger
- Department of Informatics, Technical University of Munich, Germany; Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Chunliang Wang
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Sweden
| | - Leon Weninger
- Institute of Imaging & Computer Vision, RWTH Aachen University, Germany
| | - Jianrong Wu
- Tencent Healthcare (Shenzhen) Co., Ltd, China
| | | | - Xiaoping Yang
- Department of Mathematics, Nanjing University, China
| | - Simon Chun-Ho Yu
- Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong, China
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, NY, USA
| | - Miao Yue
- CGG Services (Singapore) Pte. Ltd., Singapore
| | - Liping Zhang
- Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong, China
| | - Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Rickmer Braren
- German Cancer Consortium (DKTK), Germany; Institute for diagnostic and interventional radiology, Klinikum rechts der Isar, Technical University of Munich, Germany; Comprehensive Cancer Center Munich, Munich, Germany
| | - Volker Heinemann
- Department of Hematology/Oncology & Comprehensive Cancer Center Munich, LMU Klinikum Munich, Germany
| | | | - An Tang
- Department of Radiology, Radiation Oncology and Nuclear Medicine, University of Montréal, Canada
| | | | - Luc Soler
- Department of Surgical Data Science, Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD), France
| | - Bram van Ginneken
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel-Aviv University, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, the Hebrew University of Jerusalem, Israel
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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23
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Ezhov I, Scibilia K, Franitza K, Steinbauer F, Shit S, Zimmer L, Lipkova J, Kofler F, Paetzold JC, Canalini L, Waldmannstetter D, Menten MJ, Metz M, Wiestler B, Menze B. Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling. Med Image Anal 2023; 83:102672. [PMID: 36395623 DOI: 10.1016/j.media.2022.102672] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 07/18/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
Abstract
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains.
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Affiliation(s)
- Ivan Ezhov
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany.
| | | | | | | | - Suprosanna Shit
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | - Lucas Zimmer
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Department of Quantitative Biomedicine, UZH, Zurich, Switzerland
| | - Jana Lipkova
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Broad Institute of Harvard and MIT, Cambridge, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, USA
| | - Florian Kofler
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Johannes C Paetzold
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | | | | | - Martin J Menten
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | - Marie Metz
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Benedikt Wiestler
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Bjoern Menze
- Department of Quantitative Biomedicine, UZH, Zurich, Switzerland
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24
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Buchner JA, Kofler F, Etzel L, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Wolff R, Eitz KA, Combs SE, Bernhardt D, Wiestler B, Peeken JC. Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study. Radiother Oncol 2023; 178:109425. [PMID: 36442609 DOI: 10.1016/j.radonc.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n = 88) including four centers. RESULTS Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.
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Affiliation(s)
- Josef A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Florian Kofler
- Department of Informatics, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian M Christ
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Thomas B Brunner
- Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - Andrea Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - Björn Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Rami A El Shafie
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
| | - Susanne Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Katrin Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Horst J Feldmann
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig Holstein, Kiel, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Robert Wolff
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Guestrow, Germany; Department of Neurosurgery, University Hospital Frankfurt, Frankfurt, Germany
| | - Kerstin A Eitz
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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Bhatia HS, Brunner AD, Öztürk F, Kapoor S, Rong Z, Mai H, Thielert M, Ali M, Al-Maskari R, Paetzold JC, Kofler F, Todorov MI, Molbay M, Kolabas ZI, Negwer M, Hoeher L, Steinke H, Dima A, Gupta B, Kaltenecker D, Caliskan ÖS, Brandt D, Krahmer N, Müller S, Lichtenthaler SF, Hellal F, Bechmann I, Menze B, Theis F, Mann M, Ertürk A. Spatial proteomics in three-dimensional intact specimens. Cell 2022; 185:5040-5058.e19. [PMID: 36563667 DOI: 10.1016/j.cell.2022.11.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/13/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022]
Abstract
Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.
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Affiliation(s)
- Harsharan Singh Bhatia
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Andreas-David Brunner
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Str. 65, D-88400 Biberach Riss, Germany
| | - Furkan Öztürk
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Saketh Kapoor
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Zhouyi Rong
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Hongcheng Mai
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Marvin Thielert
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Mayar Ali
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany
| | - Rami Al-Maskari
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany
| | - Johannes Christian Paetzold
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Florian Kofler
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Helmholtz AI, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Department of Neuroradiology, Klinikum rechts der Isar, 81675 Munich, Germany
| | - Mihail Ivilinov Todorov
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Muge Molbay
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Zeynep Ilgin Kolabas
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany
| | - Moritz Negwer
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Luciano Hoeher
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Hanno Steinke
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Alina Dima
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany
| | - Basavdatta Gupta
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Doris Kaltenecker
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Institute for Diabetes and Cancer, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Özüm Sehnaz Caliskan
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Daniel Brandt
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Natalie Krahmer
- Institute for Diabetes and Obesity, Helmholz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research, Helmholz Zentrum München, 85764 Neuherberg, Germany
| | - Stephan Müller
- German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Stefan Frieder Lichtenthaler
- Graduate School of Neuroscience (GSN), 82152 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), 81377 Munich, Germany; Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Farida Hellal
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Ingo Bechmann
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Bjoern Menze
- Center for Translational Cancer Research (TranslaTUM) of the TUM, 81675 Munich, Germany; Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, 85748 Garching, Germany; Department for Quantitative Biomedicine, University of Zurich, 8006 Zurich, Switzerland
| | - Fabian Theis
- Institute of Computational Biology, Helmholz Zentrum München, 85764 Neuherberg, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany; Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Matthias Mann
- Department for Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
| | - Ali Ertürk
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, 81377 Munich, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
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26
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Hernandez Petzsche MR, de la Rosa E, Hanning U, Wiest R, Valenzuela W, Reyes M, Meyer M, Liew SL, Kofler F, Ezhov I, Robben D, Hutton A, Friedrich T, Zarth T, Bürkle J, Baran TA, Menze B, Broocks G, Meyer L, Zimmer C, Boeckh-Behrens T, Berndt M, Ikenberg B, Wiestler B, Kirschke JS. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Sci Data 2022; 9:762. [PMID: 36496501 PMCID: PMC9741583 DOI: 10.1038/s41597-022-01875-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
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Affiliation(s)
- Moritz R. Hernandez Petzsche
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ezequiel de la Rosa
- grid.435381.8icometrix, Leuven, Belgium ,grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany
| | - Uta Hanning
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Roland Wiest
- grid.5734.50000 0001 0726 5157Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Waldo Valenzuela
- grid.5734.50000 0001 0726 5157Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- grid.5734.50000 0001 0726 5157ARTORG Center for Biomedical Engineering Research, Univ. of Bern, Bern, Switzerland
| | | | - Sook-Lei Liew
- grid.42505.360000 0001 2156 6853Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA USA
| | - Florian Kofler
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany ,Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Ivan Ezhov
- grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | | | - Alexandre Hutton
- grid.42505.360000 0001 2156 6853Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA USA
| | - Tassilo Friedrich
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Teresa Zarth
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Johannes Bürkle
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - The Anh Baran
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Björn Menze
- grid.6936.a0000000123222966Department of Informatics, Technical University of Munich, Munich, Germany ,grid.7400.30000 0004 1937 0650Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Gabriel Broocks
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lukas Meyer
- grid.13648.380000 0001 2180 3484Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus Zimmer
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Boeckh-Behrens
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Maria Berndt
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benno Ikenberg
- grid.6936.a0000000123222966Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- grid.6936.a0000000123222966Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany ,grid.6936.a0000000123222966TranslaTUM – Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
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27
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Canalini L, Klein J, Waldmannstetter D, Kofler F, Cerri S, Hering A, Heldmann S, Schlaeger S, Menze BH, Wiestler B, Kirschke J, Hahn HK. Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment. Front Neuroimaging 2022; 1:977491. [PMID: 37555157 PMCID: PMC10406206 DOI: 10.3389/fnimg.2022.977491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/15/2022] [Indexed: 08/10/2023]
Abstract
Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.
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Affiliation(s)
- Luca Canalini
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jan Klein
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Diana Waldmannstetter
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Florian Kofler
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Stefano Cerri
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Alessa Hering
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Stefan Heldmann
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Sarah Schlaeger
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Bjoern H. Menze
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan Kirschke
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Horst K. Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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29
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Ezhov I, Mot T, Shit S, Lipkova J, Paetzold JC, Kofler F, Pellegrini C, Kollovieh M, Navarro F, Li H, Metz M, Wiestler B, Menze B. Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models. IEEE Trans Med Imaging 2022; 41:1269-1278. [PMID: 34928790 DOI: 10.1109/tmi.2021.3136582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate.
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30
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Thomas MF, Kofler F, Grundl L, Finck T, Li H, Zimmer C, Menze B, Wiestler B. Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans. Invest Radiol 2022; 57:187-193. [PMID: 34652289 DOI: 10.1097/rli.0000000000000828] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. MATERIALS AND METHODS Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to synthesize either missing FLAIR, T2-weighted, T1-weighted (T1w), or contrast-enhanced T1w images from available sequences, using a novel tumor-targeting loss to improve synthesis of tumor areas. We validated performance in a test set from both the REMBRANDT repository and our local institution (n = 68 WHO II-IV gliomas), using qualitative image appearance metrics, but also segmentation performance with state-of-the-art segmentation models. Segmentation of synthetic images was compared with 2 commonly used strategies for handling missing input data, entering a blank mask or copying an existing sequence. RESULTS Across tumor areas and missing sequences, synthetic images generally outperformed both conventional approaches, in particular when FLAIR was missing. Here, for edema and whole tumor segmentation, we improved the Dice score, a common metric for evaluation of segmentation performance, by 12% and 11%, respectively, over the best conventional method. No method was able to reliably replace missing contrast-enhanced T1w images. DISCUSSION Replacing missing nonenhanced magnetic resonance sequences via synthetic images significantly improves segmentation quality over most conventional approaches. This model is freely available and facilitates more widespread use of automated segmentation in routine clinical use, where missing sequences are common.
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Affiliation(s)
- Marie Franziska Thomas
- From the Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich
| | | | - Lioba Grundl
- From the Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich
| | - Tom Finck
- From the Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich
| | - Hongwei Li
- Image-Based Biomedical Modeling, Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Garching
| | - Claus Zimmer
- From the Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich
| | - Björn Menze
- Image-Based Biomedical Modeling, Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, Garching
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31
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Kofler F, Ezhov I, Fidon L, Pirkl CM, Paetzold JC, Burian E, Pati S, El Husseini M, Navarro F, Shit S, Kirschke J, Bakas S, Zimmer C, Wiestler B, Menze BH. Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles. Front Neurosci 2022; 15:752780. [PMID: 35035351 PMCID: PMC8757043 DOI: 10.3389/fnins.2021.752780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.
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Affiliation(s)
- Florian Kofler
- Department of Informatics, Technical University Munich, Munich, Germany.,Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Carolin M Pirkl
- Department of Informatics, Technical University Munich, Munich, Germany
| | - Johannes C Paetzold
- Department of Informatics, Technical University Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sarthak Pati
- Department of Informatics, Technical University Munich, Munich, Germany.,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Pennsylvania, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, United States
| | - Malek El Husseini
- Department of Informatics, Technical University Munich, Munich, Germany.,Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Fernando Navarro
- Department of Informatics, Technical University Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany.,Department of Radio Oncology and Radiation Therapy, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Suprosanna Shit
- Department of Informatics, Technical University Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Pennsylvania, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, United States.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, United States
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bjoern H Menze
- Department of Informatics, Technical University Munich, Munich, Germany.,Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
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32
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Zhylka A, Sollmann N, Kofler F, Radwan A, De Luca A, Gempt J, Wiestler B, Menze B, Krieg SM, Zimmer C, Kirschke JS, Sunaert S, Leemans A, Pluim JPW. Tracking the Corticospinal Tract in Patients With High-Grade Glioma: Clinical Evaluation of Multi-Level Fiber Tracking and Comparison to Conventional Deterministic Approaches. Front Oncol 2022; 11:761169. [PMID: 34970486 PMCID: PMC8712728 DOI: 10.3389/fonc.2021.761169] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 12/11/2022] Open
Abstract
While the diagnosis of high-grade glioma (HGG) is still associated with a considerably poor prognosis, neurosurgical tumor resection provides an opportunity for prolonged survival and improved quality of life for affected patients. However, successful tumor resection is dependent on a proper surgical planning to avoid surgery-induced functional deficits whilst achieving a maximum extent of resection (EOR). With diffusion magnetic resonance imaging (MRI) providing insight into individual white matter neuroanatomy, the challenge remains to disentangle that information as correctly and as completely as possible. In particular, due to the lack of sensitivity and accuracy, the clinical value of widely used diffusion tensor imaging (DTI)-based tractography is increasingly questioned. We evaluated whether the recently developed multi-level fiber tracking (MLFT) technique can improve tractography of the corticospinal tract (CST) in patients with motor-eloquent HGGs. Forty patients with therapy-naïve HGGs (mean age: 62.6 ± 13.4 years, 57.5% males) and preoperative diffusion MRI [repetition time (TR)/echo time (TE): 5000/78 ms, voxel size: 2x2x2 mm3, one volume at b=0 s/mm2, 32 volumes at b=1000 s/mm2] underwent reconstruction of the CST of the tumor-affected and unaffected hemispheres using MLFT in addition to deterministic DTI-based and deterministic constrained spherical deconvolution (CSD)-based fiber tractography. The brain stem was used as a seeding region, with a motor cortex mask serving as a target region for MLFT and a region of interest (ROI) for the other two algorithms. Application of the MLFT method substantially improved bundle reconstruction, leading to CST bundles with higher radial extent compared to the two other algorithms (delineation of CST fanning with a wider range; median radial extent for tumor-affected vs. unaffected hemisphere – DTI: 19.46° vs. 18.99°, p=0.8931; CSD: 30.54° vs. 27.63°, p=0.0546; MLFT: 81.17° vs. 74.59°, p=0.0134). In addition, reconstructions by MLFT and CSD-based tractography nearly completely included respective bundles derived from DTI-based tractography, which was however favorable for MLFT compared to CSD-based tractography (median coverage of the DTI-based CST for affected vs. unaffected hemispheres – CSD: 68.16% vs. 77.59%, p=0.0075; MLFT: 93.09% vs. 95.49%; p=0.0046). Thus, a more complete picture of the CST in patients with motor-eloquent HGGs might be achieved based on routinely acquired diffusion MRI data using MLFT.
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Affiliation(s)
- Andrey Zhylka
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany.,Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Ahmed Radwan
- Department of Imaging and Pathology, Translational MRI, Katholieke Universiteit (KU) Leuven, Leuven, Belgium.,Department of Neurosciences, Leuven Brain Institute (LBI), Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany.,Department of Quantitative Biomedicine, University of Zurich (UZ), Zurich, Switzerland
| | - Sandro M Krieg
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stefan Sunaert
- Department of Imaging and Pathology, Translational MRI, Katholieke Universiteit (KU) Leuven, Leuven, Belgium.,Department of Neurosciences, Leuven Brain Institute (LBI), Katholieke Universiteit (KU) Leuven, Leuven, Belgium.,Department of Radiology, Universitair Ziekenhuis (UZ) Leuven, Leuven, Belgium
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Josien P W Pluim
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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33
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Liesche-Starnecker F, Mayer K, Kofler F, Baur S, Schmidt-Graf F, Kempter J, Prokop G, Pfarr N, Wei W, Gempt J, Combs SE, Zimmer C, Meyer B, Wiestler B, Schlegel J. Immunohistochemically Characterized Intratumoral Heterogeneity Is a Prognostic Marker in Human Glioblastoma. Cancers (Basel) 2020; 12:cancers12102964. [PMID: 33066251 PMCID: PMC7602025 DOI: 10.3390/cancers12102964] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/02/2020] [Accepted: 10/09/2020] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Intratumoral heterogeneity is believed to contribute to the immense therapy resistance and recurrence rate of glioblastoma. The aim of this retrospective study was to analyze the heterogeneity of 36 human glioblastoma samples on a morphological level by immunohistochemistry. We confirmed that this method is valid for heterogeneity detection. 115 Areas of Interest were labelled. By cluster analysis, we defined two subtypes (“classical” and “mesenchymal”). The results of epigenomic analyses corroborated the findings. Interestingly, patients with tumors that consisted of both subtypes (“subtype-heterogeneous”) showed a shorter overall survival compared to patients with tumor that were dominated by one subtype (“subtype-dominant”). Furthermore, the analysis of 21 corresponding pairs of primary and recurrent glioblastoma demonstrated that, additionally to an intratumoral heterogeneity, there is also a chronological heterogeneity with dominance of the mesenchymal subtype in recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in glioblastoma and makes this hallmark assessable by routine diagnostics. Abstract Tumor heterogeneity is considered to be a hallmark of glioblastoma (GBM). Only more recently, it has become apparent that GBM is not only heterogeneous between patients (intertumoral heterogeneity) but more importantly, also within individual patients (intratumoral heterogeneity). In this study, we focused on assessing intratumoral heterogeneity. For this purpose, the heterogeneity of 38 treatment-naïve GBM was characterized by immunohistochemistry. Perceptible areas were rated for ALDH1A3, EGFR, GFAP, Iba1, Olig2, p53, and Mib1. By clustering methods, two distinct groups similar to subtypes described in literature were detected. The classical subtype featured a strong EGFR and Olig2 positivity, whereas the mesenchymal subtype displayed a strong ALDH1A3 expression and a high fraction of Iba1-positive microglia. 18 tumors exhibited both subtypes and were classified as “subtype-heterogeneous”, whereas the areas of the other tumors were all assigned to the same cluster and named “subtype-dominant”. Results of epigenomic analyses corroborated these findings. Strikingly, the subtype-heterogeneous tumors showed a clearly shorter overall survival compared to subtype-dominant tumors. Furthermore, 21 corresponding pairs of primary and recurrent GBM were compared, showing a dominance of the mesenchymal subtype in the recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in GBM, and more importantly, makes this hallmark assessable by routine diagnostics.
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Affiliation(s)
- Friederike Liesche-Starnecker
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
- Correspondence: ; Tel.: +49-89-6145
| | - Karoline Mayer
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (F.K.); (C.Z.); (B.W.)
| | - Sandra Baur
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
| | - Friederike Schmidt-Graf
- Department of Neurology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (F.S.-G.); (J.K.)
| | - Johanna Kempter
- Department of Neurology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (F.S.-G.); (J.K.)
| | - Georg Prokop
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
| | - Nicole Pfarr
- Institute of Pathology, School of Medicine, Technical University Munich, Trogerstraße 18, 81675 München, Germany;
| | - Wu Wei
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (J.G.); (B.M.)
| | - Stephanie E. Combs
- Department of RadiationOncology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany;
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (F.K.); (C.Z.); (B.W.)
| | - Bernhard Meyer
- Department of Neurosurgery, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (J.G.); (B.M.)
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University Munich, Ismaninger Str. 22, 81675 München, Germany; (F.K.); (C.Z.); (B.W.)
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Einsteinstraße 25, 81675 München, Germany
| | - Jürgen Schlegel
- Department of Neuropathology, School of Medicine, Institute of Pathology, Technical University Munich, Trogerstraße 18, 81675 München, Germany; (K.M.); (S.B.); (G.P.); (W.W.); (J.S.)
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34
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Kofler F, Berger C, Waldmannstetter D, Lipkova J, Ezhov I, Tetteh G, Kirschke J, Zimmer C, Wiestler B, Menze BH. BraTS Toolkit: Translating BraTS Brain Tumor Segmentation Algorithms Into Clinical and Scientific Practice. Front Neurosci 2020; 14:125. [PMID: 32410929 PMCID: PMC7201293 DOI: 10.3389/fnins.2020.00125] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 01/31/2020] [Indexed: 01/01/2023] Open
Abstract
Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major challenge. Several factors impede successful implementations, including data standardization and preprocessing. However, these steps are pivotal for the deployment of state-of-the-art image segmentation algorithms. To overcome these issues, we present BraTS Toolkit. BraTS Toolkit is a holistic approach to brain tumor segmentation and consists of three components: First, the BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction. Second, BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations. Finally, Brats Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion. The capabilities of our tools are illustrated with a practical example to enable easy translation to clinical and scientific practice.
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Affiliation(s)
- Florian Kofler
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Christoph Berger
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Diana Waldmannstetter
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jana Lipkova
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Giles Tetteh
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jan Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum rechts der Isar, Munich, Germany
| | - Bjoern H Menze
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
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35
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Li H, Paetzold JC, Sekuboyina A, Kofler F, Zhang J, Kirschke JS, Wiestler B, Menze B. DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis. Lecture Notes in Computer Science 2019. [DOI: 10.1007/978-3-030-32251-9_87] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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