1
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Eckhoff JA, Rosman G, Altieri MS, Speidel S, Stoyanov D, Anvari M, Meier-Hein L, März K, Jannin P, Pugh C, Wagner M, Witkowski E, Shaw P, Madani A, Ban Y, Ward T, Filicori F, Padoy N, Talamini M, Meireles OR. SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education). Surg Endosc 2023; 37:8690-8707. [PMID: 37516693 PMCID: PMC10616217 DOI: 10.1007/s00464-023-10288-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 05/04/2023] [Accepted: 07/05/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. METHODS Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. RESULTS The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. CONCLUSION This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.
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Affiliation(s)
- Jennifer A Eckhoff
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA.
- Department of General, Visceral, Tumor and Transplant Surgery, University Hospital Cologne, Kerpenerstrasse 62, 50937, Cologne, Germany.
| | - Guy Rosman
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA
| | - Maria S Altieri
- Stony Brook University Hospital, Washington University in St. Louis, 101 Nicolls Rd, Stony Brook, NY, 11794, USA
| | - Stefanie Speidel
- National Center for Tumor Diseases (NCT), Fiedlerstraße 23, 01307, Dresden, Germany
| | - Danail Stoyanov
- University College London, 43-45 Foley Street, London, W1W 7TY, UK
| | - Mehran Anvari
- Center for Surgical Invention and Innovation, Department of Surgery, McMaster University, Hamilton, ON, Canada
| | - Lena Meier-Hein
- German Cancer Research Center, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Keno März
- German Cancer Research Center, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Pierre Jannin
- MediCIS, University of Rennes - Campus Beaulieu, 2 Av. du Professeur Léon Bernard, 35043, Rennes, France
| | - Carla Pugh
- Department of Surgery, Stanford School of Medicine, 291 Campus Drive, Stanford, CA, 94305, USA
| | - Martin Wagner
- Department of Surgery, University Hospital Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Elan Witkowski
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA
| | - Paresh Shaw
- New York University Langone, 530 1St Ave. Floor 12, New York, NY, 10016, USA
| | - Amin Madani
- Surgical Artifcial Intelligence Research Academy, Department of Surgery, University Health Network, Toronto, ON, Canada
| | - Yutong Ban
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA
| | - Thomas Ward
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA
| | - Filippo Filicori
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, New York, NY, USA
| | - Nicolas Padoy
- Ihu Strasbourg - Institute Surgery Guided Par L'image, 1 Pl. de L'Hôpital, 67000, Strasbourg, France
| | - Mark Talamini
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Ozanan R Meireles
- Surgical Artificial Intelligence and Innovation Laboratory, Department of Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC339, Boston, MA, 02114, USA
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Maier-Hein L, Eisenmann M, Sarikaya D, März K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-Nötzel D, Kenngott HG, Kikinis R, Mündermann L, Navab N, Onogur S, Roß T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, Ückert F, Müller-Stich BP, Jannin P, Speidel S. Surgical data science - from concepts toward clinical translation. Med Image Anal 2022; 76:102306. [PMID: 34879287 PMCID: PMC9135051 DOI: 10.1016/j.media.2021.102306] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [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: 11/10/2020] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 02/06/2023]
Abstract
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
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Affiliation(s)
- Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany.
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Duygu Sarikaya
- Department of Computer Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey; LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Keno März
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Anand Malpani
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Hubertus Feussner
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stamatia Giannarou
- The Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom
| | - Pietro Mascagni
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | | | - Adrian Park
- Department of Surgery, Anne Arundel Health System, Annapolis, Maryland, USA; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Swaroop S Vedula
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Kevin Cleary
- The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, D.C., USA
| | | | - Germain Forestier
- L'Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), University of Haute-Alsace, Mulhouse, France; Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Bernard Gibaud
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Teodor Grantcharov
- University of Toronto, Toronto, Ontario, Canada; The Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, Canada
| | - Makoto Hashizume
- Kyushu University, Fukuoka, Japan; Kitakyushu Koga Hospital, Fukuoka, Japan
| | - Doreen Heckmann-Nötzel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hannes G Kenngott
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Sinan Onogur
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Roß
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Russell H Taylor
- Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Minu D Tizabi
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Wagner
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Gregory D Hager
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | - Justin Collins
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, Leipzig University Hospital, Leipzig, Germany
| | - Jan Goedeke
- Pediatric Surgery, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Daniel A Hashimoto
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA; Surgical AI and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Luc Joyeux
- My FetUZ Fetal Research Center, Department of Development and Regeneration, Biomedical Sciences, KU Leuven, Leuven, Belgium; Center for Surgical Technologies, Faculty of Medicine, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, Division Woman and Child, Fetal Medicine Unit, University Hospitals Leuven, Leuven, Belgium; Michael E. DeBakey Department of Surgery, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Kyle Lam
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Daniel R Leff
- Department of BioSurgery and Surgical Technology, Imperial College London, London, United Kingdom; Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Breast Unit, Imperial Healthcare NHS Trust, London, United Kingdom
| | - Amin Madani
- Department of Surgery, University Health Network, Toronto, Ontario, Canada
| | - Hani J Marcus
- National Hospital for Neurology and Neurosurgery, and UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Ozanan Meireles
- Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Seitel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, City Hospital Karlsruhe, Karlsruhe, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, Hamburg University Hospital, Hamburg, Germany
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Pierre Jannin
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Stefanie Speidel
- Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany; Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Dresden, Germany
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3
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Görtz M, Byczkowski M, Rath M, Schütz V, Reimold P, Gasch C, Simpfendörfer T, März K, Seitel A, Nolden M, Ross T, Mindroc-Filimon D, Michael D, Metzger J, Onogur S, Speidel S, Mündermann L, Fallert J, Müller M, von Knebel Doeberitz M, Teber D, Seitz P, Maier-Hein L, Duensing S, Hohenfellner M. A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study. JMIR Med Inform 2022; 10:e27743. [PMID: 35049510 PMCID: PMC8814925 DOI: 10.2196/27743] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 06/25/2021] [Accepted: 11/21/2021] [Indexed: 11/25/2022] Open
Abstract
Background Although digital and data-based technologies are widespread in various industries in the context of Industry 4.0, the use of smart connected devices in health care is still in its infancy. Innovative solutions for the medical environment are affected by difficult access to medical device data and high barriers to market entry because of proprietary systems. Objective In the proof-of-concept project OP 4.1, we show the business viability of connecting and augmenting medical devices and data through software add-ons by giving companies a technical and commercial platform for the development, implementation, distribution, and billing of innovative software solutions. Methods The creation of a central platform prototype requires the collaboration of several independent market contenders, including medical users, software developers, medical device manufacturers, and platform providers. A dedicated consortium of clinical and scientific partners as well as industry partners was set up. Results We demonstrate the successful development of the prototype of a user-centric, open, and extensible platform for the intelligent support of processes starting with the operating room. By connecting heterogeneous data sources and medical devices from different manufacturers and making them accessible for software developers and medical users, the cloud-based platform OP 4.1 enables the augmentation of medical devices and procedures through software-based solutions. The platform also allows for the demand-oriented billing of apps and medical devices, thus permitting software-based solutions to fast-track their economic development and become commercially successful. Conclusions The technology and business platform OP 4.1 creates a multisided market for the successful development, implementation, distribution, and billing of new software solutions in the operating room and in the health care sector in general. Consequently, software-based medical innovation can be translated into clinical routine quickly, efficiently, and cost-effectively, optimizing the treatment of patients through smartly assisted procedures.
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Affiliation(s)
- Magdalena Görtz
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Mathias Rath
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Reimold
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany
| | - Claudia Gasch
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Keno März
- German Cancer Research Center, Heidelberg, Germany
| | | | - Marco Nolden
- German Cancer Research Center, Heidelberg, Germany
| | - Tobias Ross
- German Cancer Research Center, Heidelberg, Germany
| | | | | | | | - Sinan Onogur
- German Cancer Research Center, Heidelberg, Germany
| | | | | | | | | | - Magnus von Knebel Doeberitz
- Department of Applied Tumor Biology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | | | | | - Stefan Duensing
- Section of Molecular Urooncology, Department of Urology, University of Heidelberg School of Medicine, Heidelberg, Germany
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4
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Abstract
The increasing networking of data systems in medicine is not only leading to modern interdisciplinarity in the sense of cooperation between different medical departments, but also poses new challenges regarding the building and room infrastructure. The surgical operating room of the future expands or augments its reality, away from the pure building characteristics, towards an intelligent and communicative space platform. The building infrastructure (operating theatre) serves as sensor and actuator. Thus, it is possible to inform about missing diagnostics as well as to register them directly in the contextualization of the planned surgical intervention or to integrate them into the processes. Integrated operating theatres represent a comprehensive computer platform based on a corresponding system architecture with software-based protocols. An underlying modular system consisting of various modules for image acquisition and analysis, interaction and visualization supports the integration and merging of heterogeneous data that are generated in a hospital operation. Integral building data (e.g., air conditioning, lighting control, device registration) are merged with patient-related data (age, type of illness, concomitant diseases, existing diagnostic CT and MRI images). New systems coming onto the market, as well as already existing systems will have to be measured by the extent to which they will be able to guarantee this integration of information-similar to the development from mobile phone to smartphone. Cost reduction should not be the only legitimizing argument for the market launch, but the vision of a new quality of surgical perception and action.
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Affiliation(s)
- D Teber
- Urologische Klinik, Städtisches Klinikum Karlsruhe, Moltkestr. 90, 76133, Karlsruhe, Deutschland.
| | - C Engels
- Urologische Klinik, Städtisches Klinikum Karlsruhe, Moltkestr. 90, 76133, Karlsruhe, Deutschland
| | - L Maier-Hein
- Abteilung Computer-assistierte Medizinische Interventionen (CAMI), Deutsches Krebsforschungszentrum, Heidelberg, Deutschland
| | - L Ayala
- Abteilung Computer-assistierte Medizinische Interventionen (CAMI), Deutsches Krebsforschungszentrum, Heidelberg, Deutschland
| | - S Onogur
- Abteilung Computer-assistierte Medizinische Interventionen (CAMI), Deutsches Krebsforschungszentrum, Heidelberg, Deutschland
| | - A Seitel
- Abteilung Computer-assistierte Medizinische Interventionen (CAMI), Deutsches Krebsforschungszentrum, Heidelberg, Deutschland
| | - K März
- Abteilung Computer-assistierte Medizinische Interventionen (CAMI), Deutsches Krebsforschungszentrum, Heidelberg, Deutschland
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5
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Maier-Hein L, Eisenmann M, Reinke A, Onogur S, Stankovic M, Scholz P, Arbel T, Bogunovic H, Bradley AP, Carass A, Feldmann C, Frangi AF, Full PM, van Ginneken B, Hanbury A, Honauer K, Kozubek M, Landman BA, März K, Maier O, Maier-Hein K, Menze BH, Müller H, Neher PF, Niessen W, Rajpoot N, Sharp GC, Sirinukunwattana K, Speidel S, Stock C, Stoyanov D, Taha AA, van der Sommen F, Wang CW, Weber MA, Zheng G, Jannin P, Kopp-Schneider A. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat Commun 2018; 9:5217. [PMID: 30523263 PMCID: PMC6284017 DOI: 10.1038/s41467-018-07619-7] [Citation(s) in RCA: 143] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 11/07/2018] [Indexed: 11/08/2022] Open
Abstract
International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future.
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Affiliation(s)
- Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Annika Reinke
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Sinan Onogur
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Marko Stankovic
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Patrick Scholz
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Tal Arbel
- Centre for Intelligent Machines, McGill University, Montreal, QC, H3A0G4, Canada
| | - Hrvoje Bogunovic
- Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University Vienna, 1090, Vienna, Austria
| | - Andrew P Bradley
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, 4001, Australia
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Carolin Feldmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Alejandro F Frangi
- CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine, The University of Leeds, Leeds, Yorkshire, LS2 9JT, UK
| | - Peter M Full
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Medical Image Analysis, Radboud University Center, 6525 GA, Nijmegen, The Netherlands
| | - Allan Hanbury
- Institute of Information Systems Engineering, TU Wien, 1040, Vienna, Austria
- Complexity Science Hub Vienna, 1080, Vienna, Austria
| | - Katrin Honauer
- Heidelberg Collaboratory for Image Processing (HCI), Heidelberg University, 69120, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Masaryk University, 60200, Brno, Czech Republic
| | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, TN, 37235-1679, USA
| | - Keno März
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Oskar Maier
- Institute of Medical Informatics, Universität zu Lübeck, 23562, Lübeck, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Bjoern H Menze
- Institute for Advanced Studies, Department of Informatics, Technical University of Munich, 80333, Munich, Germany
| | - Henning Müller
- Information System Institute, HES-SO, Sierre, 3960, Switzerland
| | - Peter F Neher
- Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Wiro Niessen
- Departments of Radiology, Nuclear Medicine and Medical Informatics, Erasmus MC, 3015 GD, Rotterdam, The Netherlands
| | - Nasir Rajpoot
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Gregory C Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | - Stefanie Speidel
- Division of Translational Surgical Oncology (TCO), National Center for Tumor Diseases Dresden, 01307, Dresden, Germany
| | - Christian Stock
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Danail Stoyanov
- Centre for Medical Image Computing (CMIC) & Department of Computer Science, University College London, London, W1W 7TS, UK
| | - Abdel Aziz Taha
- Data Science Studio, Research Studios Austria FG, 1090, Vienna, Austria
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
| | - Ching-Wei Wang
- AIExplore, NTUST Center of Computer Vision and Medical Imaging, Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 106, Taiwan
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, University Medical Center Rostock, 18051, Rostock, Germany
| | - Guoyan Zheng
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, 3014, Switzerland
| | - Pierre Jannin
- Univ Rennes, Inserm, LTSI (Laboratoire Traitement du Signal et de l'Image) - UMR_S 1099, Rennes, 35043, Cedex, France
| | - Annette Kopp-Schneider
- Division of Biostatistics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
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6
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Heim E, Roß T, Seitel A, März K, Stieltjes B, Eisenmann M, Lebert J, Metzger J, Sommer G, Sauter AW, Schwartz FR, Termer A, Wagner F, Kenngott HG, Maier-Hein L. Large-scale medical image annotation with crowd-powered algorithms. J Med Imaging (Bellingham) 2018; 5:034002. [PMID: 30840724 DOI: 10.1117/1.jmi.5.3.034002] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [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: 04/13/2018] [Accepted: 07/26/2018] [Indexed: 01/07/2023] Open
Abstract
Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.
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Affiliation(s)
- Eric Heim
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
| | - Tobias Roß
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
| | - Alexander Seitel
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
| | - Keno März
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
| | - Bram Stieltjes
- University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
| | - Johannes Lebert
- University of Heidelberg, Department of General, Visceral and Transplant Surgery, Heidelberg, Germany
| | - Jasmin Metzger
- German Cancer Research Center (DKFZ), Medical Image Computing, Heidelberg, Germany
| | - Gregor Sommer
- University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland
| | - Alexander W Sauter
- University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland
| | - Fides Regina Schwartz
- University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland
| | - Andreas Termer
- University of Heidelberg, Department of General, Visceral and Transplant Surgery, Heidelberg, Germany
| | - Felix Wagner
- University of Heidelberg, Department of General, Visceral and Transplant Surgery, Heidelberg, Germany
| | - Hannes Götz Kenngott
- University of Heidelberg, Department of General, Visceral and Transplant Surgery, Heidelberg, Germany
| | - Lena Maier-Hein
- German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany
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7
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Gibaud B, Forestier G, Feldmann C, Ferrigno G, Gonçalves P, Haidegger T, Julliard C, Katić D, Kenngott H, Maier-Hein L, März K, de Momi E, Nagy DÁ, Nakawala H, Neumann J, Neumuth T, Rojas Balderrama J, Speidel S, Wagner M, Jannin P. Toward a standard ontology of surgical process models. Int J Comput Assist Radiol Surg 2018; 13:1397-1408. [PMID: 30006820 DOI: 10.1007/s11548-018-1824-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/05/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. METHODS A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. RESULTS The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. CONCLUSION The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.
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Affiliation(s)
| | | | - Carolin Feldmann
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Paulo Gonçalves
- Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal.,IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Tamás Haidegger
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, Budapest, Hungary.,Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
| | - Chantal Julliard
- Inserm, LTSI - UMR_S 1099, Univ Rennes, Rennes, France.,LIRMM, Université de Montpellier, Montpellier, France.,Stryker GmbH, Freiburg, Germany
| | - Darko Katić
- Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany.,ArtiMinds Robotics GmbH, Karlsruhe, Germany
| | - Hannes Kenngott
- Department of General, Abdominal and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Lena Maier-Hein
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Keno März
- Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Dénes Ákos Nagy
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, Budapest, Hungary.,Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
| | | | - Juliane Neumann
- Innovation Center Computer Assisted Surgery, Leipzig University, Leipzig, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery, Leipzig University, Leipzig, Germany
| | | | | | - Martin Wagner
- Department of General, Abdominal and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Pierre Jannin
- Inserm, LTSI - UMR_S 1099, Univ Rennes, Rennes, France
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8
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Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, Eisenmann M, Feussner H, Forestier G, Giannarou S, Hashizume M, Katic D, Kenngott H, Kranzfelder M, Malpani A, März K, Neumuth T, Padoy N, Pugh C, Schoch N, Stoyanov D, Taylor R, Wagner M, Hager GD, Jannin P. Surgical data science for next-generation interventions. Nat Biomed Eng 2017; 1:691-696. [PMID: 31015666 DOI: 10.1038/s41551-017-0132-7] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Lena Maier-Hein
- Division Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
| | - Swaroop S Vedula
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Stefanie Speidel
- Division Translational Surgical Oncology, National Center for Tumor Diseases (NCT), 01307, Dresden, Germany
| | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, 80333, Munich, Germany.,Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02215, USA.,Department of Computer Science, University of Bremen, 28359, Bremen, Germany.,Fraunhofer MEVIS, 28359, Bremen, Germany
| | - Adrian Park
- Department of Surgery, Anne Arundel Health System, Annapolis, MD, 21401, USA.,Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Matthias Eisenmann
- Division Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Hubertus Feussner
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Germain Forestier
- Department of Computer Science, University of Haute-Alsace, 68093, Mulhouse, France
| | - Stamatia Giannarou
- The Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK
| | - Makoto Hashizume
- Department of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | - Darko Katic
- Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technolgoy (KIT), 76131, Karlsruhe, Germany
| | - Hannes Kenngott
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Michael Kranzfelder
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Anand Malpani
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, MD, 21218, USA.,Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Keno März
- Division Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103, Leipzig, Germany
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, IHU, 67081, Strasbourg, France
| | - Carla Pugh
- Department of Surgery, University of Wisconsin, Madison, WI, 53792, USA
| | - Nicolai Schoch
- Engineering Mathematics and Computing Lab (EMCL), IWR, Heidelberg University, 69120, Heidelberg, Germany
| | - Danail Stoyanov
- Centre for Medical Image Computing (CMIC) and Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Russell Taylor
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Martin Wagner
- Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, 69120, Heidelberg, Germany
| | - Gregory D Hager
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, MD, 21218, USA. .,Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Pierre Jannin
- Université de Rennes 1, 35065, Rennes, France. .,INSERM, 35043, Rennes, France.
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9
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März K, Hafezi M, Weller T, Saffari A, Nolden M, Fard N, Majlesara A, Zelzer S, Maleshkova M, Volovyk M, Gharabaghi N, Wagner M, Emami G, Engelhardt S, Fetzer A, Kenngott H, Rezai N, Rettinger A, Studer R, Mehrabi A, Maier-Hein L. Toward knowledge-based liver surgery: holistic information processing for surgical decision support. Int J Comput Assist Radiol Surg 2015; 10:749-59. [PMID: 25847671 DOI: 10.1007/s11548-015-1187-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 03/20/2015] [Indexed: 11/27/2022]
Abstract
PURPOSE Malignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies). METHODS The contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making. RESULTS Our patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of [Formula: see text] assertions per patient. CONCLUSION The proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.
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Affiliation(s)
- K März
- Department of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany,
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März K, Franz AM, Seitel A, Winterstein A, Bendl R, Zelzer S, Nolden M, Meinzer HP, Maier-Hein L. MITK-US: real-time ultrasound support within MITK. Int J Comput Assist Radiol Surg 2013; 9:411-20. [DOI: 10.1007/s11548-013-0962-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 11/05/2013] [Indexed: 11/28/2022]
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11
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Franz AM, März K, Seitel A, Kenngott HG, Wagner M, Preukschas A, Meinzer HP, Wolf I, Maier-Hein L. Combined Modality for Ultrasound Imaging and Electromagnetic Tracking. ACTA ACUST UNITED AC 2013; 58 Suppl 1:/j/bmte.2013.58.issue-s1-L/bmt-2013-4291/bmt-2013-4291.xml. [DOI: 10.1515/bmt-2013-4291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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