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De Schepper S, Gnanasegaran G, De Vos W, Van de Casteele E, Dickson JC, Van den Wyngaert T. From SPECT/CT towards absolute quantification? - the case of unilateral condylar hyperplasia of the mandible. EJNMMI Phys 2024; 11:74. [PMID: 39177939 PMCID: PMC11343952 DOI: 10.1186/s40658-024-00676-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 08/05/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND Unilateral condylar hyperplasia (UCH) of the mandible is a rare condition characterized by asymmetric growth of the mandibular condyles. Bone scintigraphy with SPECT(/CT) is commonly used to diagnose UCH and guide treatment. Still, varying results have been reported using the traditional threshold of 55%:45% in relative tracer uptake. While absolute quantification of uptake on SPECT/CT could improve results, optimal correction and reconstruction settings are currently unknown. METHODS Three anthropomorphic phantoms representing UCH were developed from patient CT volumes and produced using 3D printing technology. Fillable spherical inserts of different sizes (Ø: 8-15 mm) were placed in the condylar positions representing symmetrical and asymmetrical distributions. Recovery coefficients were determined for SPECT/CT using various reconstruction corrections, including attenuation and scatter correction (ACSC), resolution modeling (RM), and partial volume correction (PVC) using phantom measurements. Uptake ratios between condyles and condyle to clivus were evaluated. Finally, the impact of these correction techniques on absolute activity and diagnostic accuracy was assessed in a retrospective patient cohort for the diagnostic threshold of 55%:45%. RESULTS The activity was only partially recovered in all spherical inserts (range: 22.5-64.9%). However, RM improved relative recovery by 20.2-62.3% compared to ACSC. In the symmetric phantoms, the 95% confidence interval (CI) of condyle ratios included the diagnostic threshold (57.6%:42.4%) for UCH when using ACSC potentially leading to false positives, but not for ACSCRM datasets. Partial volume corrections coefficients from the NEMA IQ phantom was positionally dependent, with improvements seen performing PVC using coefficients derived from anthropomorphic phantoms. Retrospective application in a patient cohort showed only a weak linear correlation (R²: 0.25-0.67) and large limits of agreement (9.6-12.5%) between different reconstructions. Up to 44% of patients were reclassified using the 55%:45% threshold. Using clinical outcome data, ACSCRM had highest sensitivity (91%; 95% CI 59-100%) and specificity (66%; 95% CI 47-81%), significantly improving specificity (P = 0.038). CONCLUSIONS Anthropomorphic phantoms were shown to be essential in determining optimal settings for acquisition, reconstruction, and analysis. SPECT/CT reconstructions with attenuation and scatter correction and resolution modeling are recommended and could improve specificity when using the 55%:45% threshold to assess condylar growth.
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Affiliation(s)
- Stijn De Schepper
- Department of Nuclear Medicine, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, 2650, Belgium.
- Faculty of Medicine and Health Sciences (MICA - IPPON), University of Antwerp, Wilrijk, Belgium.
| | | | - Wouter De Vos
- Department of Oral and Maxillofacial Surgery, Antwerp University Hospital, Antwerp, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
| | - Elke Van de Casteele
- Department of Oral and Maxillofacial Surgery, Antwerp University Hospital, Antwerp, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
| | - John C Dickson
- Institute of Nuclear Medicine, University College of London Hospitals NHS, London, UK
| | - Tim Van den Wyngaert
- Department of Nuclear Medicine, Antwerp University Hospital, Drie Eikenstraat 655, Edegem, 2650, Belgium
- Faculty of Medicine and Health Sciences (MICA - IPPON), University of Antwerp, Wilrijk, Belgium
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Raj P, Paidi SK, Conway L, Chatterjee A, Barman I. CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S22706. [PMID: 38638450 PMCID: PMC11025678 DOI: 10.1117/1.jbo.29.s2.s22706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/20/2024]
Abstract
Significance Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x - y plane. Leveraging cell continuity in consecutive z -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.
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Affiliation(s)
- Piyush Raj
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Santosh Kumar Paidi
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Lauren Conway
- Johns Hopkins University, Department of Chemical and Biomolecular Engineering, Baltimore, Maryland, United States
| | - Arnab Chatterjee
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
| | - Ishan Barman
- Johns Hopkins University, Department of Mechanical Engineering, Baltimore, Maryland, United States
- The Johns Hopkins University, School of Medicine, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, Maryland, United States
- Johns Hopkins University, Department of Oncology, Baltimore, Maryland, United States
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Paul A, Mallidi S. U-Net enhanced real-time LED-based photoacoustic imaging. JOURNAL OF BIOPHOTONICS 2024; 17:e202300465. [PMID: 38622811 PMCID: PMC11164633 DOI: 10.1002/jbio.202300465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/18/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost-effective, smaller footprint light emitting diodes (LEDs) are rapidly becoming important PA optical sources. However, the key challenge faced by the LED-based systems is the low light fluence that is generally compensated by high frame averaging, consequently reducing acquisition frame-rate. In this study, we present a simple deep learning U-Net framework that enhances the signal-to-noise ratio (SNR) and contrast of PA image obtained by averaging low number of frames. The SNR increased by approximately four-fold for both in-class in vitro phantoms (4.39 ± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and failure to reduce the salt & pepper noise). Overall, the developed U-Net framework can provide a real-time image enhancement platform for clinically translatable low-cost and low-energy light source-based PA imaging systems.
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Affiliation(s)
- Avijit Paul
- Department of Biomedical Engineering, Tufts University, Medford, MA, USA
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4
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Ben Shabat D, Hadad A, Boruchovsky A, Yaakobi E. GradHC: highly reliable gradual hash-based clustering for DNA storage systems. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae274. [PMID: 38648049 DOI: 10.1093/bioinformatics/btae274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/27/2024] [Accepted: 04/17/2024] [Indexed: 04/25/2024]
Abstract
MOTIVATION As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. RESULTS In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results. AVAILABILITY AND IMPLEMENTATION https://github.com/bensdvir/GradHC.
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Affiliation(s)
- Dvir Ben Shabat
- Department of Computer Science, Technion, Haifa 320003, Israel
| | - Adar Hadad
- Department of Computer Science, Technion, Haifa 320003, Israel
| | | | - Eitan Yaakobi
- Department of Computer Science, Technion, Haifa 320003, Israel
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Helgesson S, Tarai S, Langner T, Ahlström H, Johansson L, Kullberg J, Lundström E. Spleen volume is independently associated with non-alcoholic fatty liver disease, liver volume and liver fibrosis. Heliyon 2024; 10:e28123. [PMID: 38665588 PMCID: PMC11043861 DOI: 10.1016/j.heliyon.2024.e28123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 04/28/2024] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) can lead to irreversible liver damage manifesting in systemic effects (e.g., elevated portal vein pressure and splenomegaly) with increased risk of deadly outcomes. However, the association of spleen volume with NAFLD and related type 2-diabetes (T2D) is not fully understood. The UK Biobank contains comprehensive health-data of 500,000 participants, including clinical data and MR images of >40,000 individuals. The present study estimated the spleen volume of 37,066 participants through automated deep learning-based image segmentation of neck-to-knee MR images. The aim was to investigate the associations of spleen volume with NAFLD, T2D and liver fibrosis, while adjusting for natural confounders. The recent redefinition and new designation of NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD), promoted by major organisations of studies on liver disease, was not employed as introduced after the conduct of this study. The results showed that spleen volume decreased with age, correlated positively with body size and was smaller in females compared to males. Larger spleens were observed in subjects with NAFLD and T2D compared to controls. Spleen volume was also positively and independently associated with liver fat fraction, liver volume and the fibrosis-4 score, with notable volumetric increases already at low liver fat fractions and volumes, but not independently associated with T2D. These results suggest a link between spleen volume and NAFLD already at an early stage of the disease, potentially due to initial rise in portal vein pressure.
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Affiliation(s)
- Samuel Helgesson
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
| | - Sambit Tarai
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Håkan Ahlström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | | | - Joel Kullberg
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
- Antaros Medical AB, BioVenture Hub, Sweden
| | - Elin Lundström
- Radiology, Department of Surgical Sciences, Uppsala University, Sweden
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Carass A, Greenman D, Dewey BE, Calabresi PA, Prince JL, Pham DL. Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI. NEUROIMAGE. REPORTS 2024; 4:100195. [PMID: 38370461 PMCID: PMC10871705 DOI: 10.1016/j.ynirp.2024.100195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Danielle Greenman
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA
| | - Blake E. Dewey
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Peter A. Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dzung L. Pham
- Department of Radiology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
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7
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Lazen P, Lima Cardoso P, Sharma S, Cadrien C, Roetzer-Pejrimovsky T, Furtner J, Strasser B, Hingerl L, Lipka A, Preusser M, Marik W, Bogner W, Widhalm G, Rössler K, Trattnig S, Hangel G. A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients. Cancers (Basel) 2024; 16:943. [PMID: 38473305 DOI: 10.3390/cancers16050943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/20/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.
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Affiliation(s)
- Philipp Lazen
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Sukrit Sharma
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Cornelius Cadrien
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Thomas Roetzer-Pejrimovsky
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Danube Private University, 3500 Krems, Austria
| | - Bernhard Strasser
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Hingerl
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Alexandra Lipka
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria
| | - Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Wolfgang Bogner
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Georg Widhalm
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
| | - Karl Rössler
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
| | - Siegfried Trattnig
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, 3100 St. Pölten, Austria
| | - Gilbert Hangel
- High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
- Department for Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for MR Imaging Biomarkers, 1090 Vienna, Austria
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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 PMCID: PMC11182665 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [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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9
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Son S, Joo B, Park M, Suh SH, Oh HS, Kim JW, Lee S, Ahn SJ, Lee JM. Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment. Front Oncol 2024; 13:1273013. [PMID: 38288101 PMCID: PMC10823345 DOI: 10.3389/fonc.2023.1273013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
Purpose/objectives Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment. Methods and materials A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment response. The detection sensitivity, precision, Dice similarity coefficient (DSC), and agreement of treatment response assessments between neuroradiologists and RLK-Unet were assessed. Results RLK-Unet demonstrated a sensitivity of 86.9% and a precision of 79.6% for BMs and had a DSC of 0.663. Segmentation performance was better in the subgroup with larger BMs (DSC, 0.843). The agreement in the response assessment for BMs between the radiologists and RLK-Unet was excellent (intraclass correlation, 0.84). Conclusion RLK-Unet yielded accurate detection and segmentation of BM and could assist clinicians in treatment response assessment.
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Affiliation(s)
- Seungyeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Hee Sang Oh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Jun Won Kim
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Seoyoung Lee
- Division of Medical Oncology, Department of Internal Medicine, Gangnam Severance Hospital, College of Medicine, Yonsei University, Seoul, Republic of Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University, College of Medicine, Seoul, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
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10
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Tzanis E, Stratakis J, Myronakis M, Damilakis J. A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT. Phys Med 2024; 117:103195. [PMID: 38048731 DOI: 10.1016/j.ejmp.2023.103195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/26/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023] Open
Abstract
PURPOSE To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT. METHODS Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed. RESULTS The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry. CONCLUSIONS The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.
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Affiliation(s)
- Eleftherios Tzanis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Stratakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - Marios Myronakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece
| | - John Damilakis
- Department of Medical Physics, School of Medicine, University of Crete, P.O. Box 2208, Heraklion, Crete 71003, Greece.
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11
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Hlouschek J, König B, Bos D, Santiago A, Zensen S, Haubold J, Pöttgen C, Herz A, Opitz M, Wetter A, Guberina M, Stuschke M, Zylka W, Kühl H, Guberina N. Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study. Diagnostics (Basel) 2023; 14:28. [PMID: 38201337 PMCID: PMC10804383 DOI: 10.3390/diagnostics14010028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/10/2023] [Accepted: 12/17/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVis) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function. Furthermore, a deep-learning algorithm for automatic contouring was applied (IV). An intermodality comparison of the above-mentioned strategies for contouring GTVis was performed. For the mean GTVref (standard deviation (SD)), the interquartile range (IQR)) was 0.68 mL (0.33; 0.34-1.1). GTV segmentation was distributed as follows: (I) 0.61 mL (0.27; 0.36-0.92); (II) 0.41 mL (0.28; 0.23-0.63); (III) 0.65 mL (0.35; 0.32-0.90); and (IV) 0.61 mL (0.29; 0.33-0.95). GTVref was found to be significantly correlated with GTVis (I) p < 0.001, r = 0.989 (III) p = 0.001, r = 0.916, and (IV) p < 0.001, r = 0.986, but not with (II) p = 0.091, r = 0.595. The Sørensen-Dice indices for the semi-automatic tools were 0.74 (I), 0.57 (II) and 0.71 (III). For the semi-automatic, conventional segmentation tools evaluated, the adaptive-brush function (I) performed closest to the reference standard (0). The automatic deep learning tool (IV) showed high performance for auto-segmentation and was close to the reference standard. For high precision radiation therapy, visual control, and, where necessary, manual correction, are mandatory for all evaluated tools.
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Affiliation(s)
- Julian Hlouschek
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Britta König
- Department of Radiology, University Hospital Muenster (UKM), Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Muenster, Germany
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Alina Santiago
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Christoph Pöttgen
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Andreas Herz
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Axel Wetter
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, Eißendorfer Pferdeweg 52, 21075 Hamburg, Germany
| | - Maja Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Martin Stuschke
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
| | - Waldemar Zylka
- Westphalian University, Campus Gelsenkirchen, Neidenburger Str. 43, 45897 Gelsenkirchen, Germany
| | - Hilmar Kühl
- Department of Radiology, St. Bernhard-Hospital Kamp-Lintfort, Bürgermeister-Schmelzing-Str. 90, 47475 Kamp-Lintfort, Germany
| | - Nika Guberina
- Department of Radiotherapy, West German Cancer Center, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
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12
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Wu CT, Yang YH, Chang YZ. Creating high-resolution 3D cranial implant geometry using deep learning techniques. Front Bioeng Biotechnol 2023; 11:1297933. [PMID: 38149174 PMCID: PMC10750412 DOI: 10.3389/fbioe.2023.1297933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/22/2023] [Indexed: 12/28/2023] Open
Abstract
Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane.
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Affiliation(s)
- Chieh-Tsai Wu
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | | | - Yau-Zen Chang
- Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Mechanical Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Mechanical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
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13
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Gao C, Landman BA, Prince JL, Carass A. Reproducibility evaluation of the effects of MRI defacing on brain segmentation. J Med Imaging (Bellingham) 2023; 10:064001. [PMID: 38074632 PMCID: PMC10704191 DOI: 10.1117/1.jmi.10.6.064001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/22/2023] [Accepted: 10/24/2023] [Indexed: 12/20/2023] Open
Abstract
Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms, such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it is encouraged to include multiple brain segmentation pipelines.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Jerry L. Prince
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
| | - Aaron Carass
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States
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14
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Wang C, Dai W. Lung nodule segmentation via semi-residual multi-resolution neural networks. Open Life Sci 2023; 18:20220727. [PMID: 37941779 PMCID: PMC10628569 DOI: 10.1515/biol-2022-0727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 11/10/2023] Open
Abstract
The integration of deep neural networks and cloud computing has become increasingly prevalent within the domain of medical image processing, facilitated by the recent strides in neural network theory and the advent of the internet of things (IoTs). This juncture has led to the emergence of numerous image segmentation networks and innovative solutions that facilitate medical practitioners in diagnosing lung cancer. Within the contours of this study, we present an end-to-end neural network model, christened as the "semi-residual Multi-resolution Convolutional Neural Network" (semi-residual MCNN), devised to engender precise lung nodule segmentation maps within the milieu of cloud computing. Central to the architecture are three pivotal features, each coalescing to effectuate a notable enhancement in predictive accuracy: the incorporation of semi-residual building blocks, the deployment of group normalization techniques, and the orchestration of multi-resolution output heads. This innovative model is systematically subjected to rigorous training and testing regimes, using the LIDC-IDRI dataset - a widely embraced and accessible repository - comprising a diverse ensemble of 1,018 distinct lung CT images tailored to the realm of lung nodule segmentation.
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Affiliation(s)
- Chenyang Wang
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Wei Dai
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
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15
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Bhowmik A, Monga N, Belen K, Varela K, Sevilimedu V, Thakur SB, Martinez DF, Sutton EJ, Pinker K, Eskreis-Winkler S. Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model. Invest Radiol 2023; 58:710-719. [PMID: 37058323 PMCID: PMC11334216 DOI: 10.1097/rli.0000000000000976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
OBJECTIVES The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. MATERIALS AND METHODS In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. RESULTS In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. CONCLUSIONS Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
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Affiliation(s)
- Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Natasha Monga
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kristin Belen
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Keitha Varela
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sunitha B. Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Danny F. Martinez
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth J. Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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16
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Gudmundson AT, Davies-Jenkins CW, Özdemir İ, Murali-Manohar S, Zöllner HJ, Song Y, Hupfeld KE, Schnitzler A, Oeltzschner G, Stark CEL, Edden RAE. Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539813. [PMID: 37215030 PMCID: PMC10197548 DOI: 10.1101/2023.05.08.539813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.
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Affiliation(s)
- Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - İpek Özdemir
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Craig E. L. Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
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17
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Jain Y, Godwin LL, Joshi S, Mandarapu S, Le T, Lindskog C, Lundberg E, Börner K. Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms. Nat Commun 2023; 14:4656. [PMID: 37537179 PMCID: PMC10400613 DOI: 10.1038/s41467-023-40291-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023] Open
Abstract
The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.
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Affiliation(s)
- Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Leah L Godwin
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Sripad Joshi
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Shriya Mandarapu
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Trang Le
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Division of Cancer Precision Medicine, Uppsala University, Uppsala, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
- Chan Zuckerberg Biohub, San Francisco, CA, 94305, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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18
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Sarmah M, Neelima A, Singh HR. Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images. Vis Comput Ind Biomed Art 2023; 6:15. [PMID: 37495817 PMCID: PMC10371974 DOI: 10.1186/s42492-023-00142-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Three-dimensional (3D) reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units. In the coming years, most patient care will shift toward this new paradigm. However, development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved, most of which are dependent on human expertise. In this review, a survey of pre-processing steps was conducted, and reconstruction techniques for several organs in medical diagnosis were studied. Various methods and principles related to 3D reconstruction were highlighted. The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.
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Affiliation(s)
- Mriganka Sarmah
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India.
| | - Arambam Neelima
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India
| | - Heisnam Rohen Singh
- Department of Information Technology, Nagaland University, Nagaland, 797112, India
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19
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Jain Y, Godwin LL, Ju Y, Sood N, Quardokus EM, Bueckle A, Longacre T, Horning A, Lin Y, Esplin ED, Hickey JW, Snyder MP, Patterson NH, Spraggins JM, Börner K. Segmentation of human functional tissue units in support of a Human Reference Atlas. Commun Biol 2023; 6:717. [PMID: 37468557 PMCID: PMC10356924 DOI: 10.1038/s42003-023-04848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/17/2023] [Indexed: 07/21/2023] Open
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale-showcasing the value of Kaggle competitions for advancing research.
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Affiliation(s)
- Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Leah L Godwin
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Yingnan Ju
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Naveksha Sood
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Teri Longacre
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Aaron Horning
- Thermo Fisher Scientific, South San Francisco, CA, 94080, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Edward D Esplin
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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20
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Derwae H, Nijs M, Geysels A, Waelkens E, De Moor B. Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis. Anal Chem 2023. [PMID: 37402207 DOI: 10.1021/acs.analchem.2c05606] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of molecules in tissues. An MSI experiment results in large amounts of high dimensional data, so efficient computational methods are needed to analyze the output. Topological Data Analysis (TDA) has proven to be effective in all kinds of applications. TDA focuses on the topology of the data in high dimensional space. Looking at the shape in a high dimensional data set can lead to new or different insights. In this work, we investigate the use of Mapper, a form of TDA, applied on MSI data. Mapper is used to find data clusters inside two healthy mouse pancreas data sets. The results are compared to previous work using UMAP for MSI data analysis on the same data sets. This work finds that the proposed technique discovers the same clusters in the data as UMAP and is also able to uncover new clusters, such as an additional ring structure inside the pancreatic islets and a better defined cluster containing blood vessels. The technique can be used for a large variety of data types and sizes and can be optimized for specific applications. It is also computationally similar to UMAP for clustering. Mapper is a very interesting method, especially its use in biomedical applications.
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Affiliation(s)
- Helena Derwae
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
| | - Melanie Nijs
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
| | - Axel Geysels
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
| | - Etienne Waelkens
- Department of Cellular and Molecular Medicine, KU Leuven, 3001 Leuven, Belgium
| | - Bart De Moor
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium
- Fellow IEEE, SIAM at STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, 3001 Leuven, Belgium
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21
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Giraldo DL, Beirinckx Q, Den Dekker AJ, Jeurissen B, Sijbers J. Super-Resolution Reconstruction of Multi-Slice T2-W FLAIR MRI Improves Multiple Sclerosis Lesion Segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082625 DOI: 10.1109/embc40787.2023.10341047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Due to acquisition time constraints, T2-w FLAIR MRI of Multiple Sclerosis (MS) patients is often acquired with multi-slice 2D protocols with a low through-plane resolution rather than with high-resolution 3D protocols. Automated lesion segmentation on such low-resolution (LR) images, however, performs poorly and leads to inaccurate lesion volume estimates. Super-resolution reconstruction (SRR) methods can then be used to obtain a high-resolution (HR) image from multiple LR images to serve as input for lesion segmentation. In this work, we evaluate the effect on MS lesion segmentation of three SRR approaches: one based on interpolation, a state-of-the-art self-supervised CNN-based strategy, and a recently proposed model-based SRR method. These SRR strategies were applied to LR acquisitions simulated from 3D T2-w FLAIR MRI of MS patients. Each SRR method was evaluated in terms of image reconstruction quality and subsequent lesion segmentation performance. When compared to segmentation on LR images, the three considered SRR strategies demonstrate improved lesion segmentation. Furthermore, in some scenarios, SRR achieves a similar segmentation performance compared to segmentation of HR images.Clinical relevance- This study demonstrates the positive impact of super-resolution reconstruction from T2-w FLAIR multi-slice MRI acquisitions on segmentation performance of MS lesions.
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22
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Gao C, Landman BA, Prince JL, Carass A. A reproducibility evaluation of the effects of MRI defacing on brain segmentation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.15.23289995. [PMID: 37293070 PMCID: PMC10246049 DOI: 10.1101/2023.05.15.23289995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and the 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it's encouraged to include multiple brain segmentation pipelines.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, 37235
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, 37235
| | - Jerry L. Prince
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, 21218
| | - Aaron Carass
- The Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, 21218
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23
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Andrearczyk V, Oreiller V, Abobakr M, Akhavanallaf A, Balermpas P, Boughdad S, Capriotti L, Castelli J, Le Rest CC, Decazes P, Correia R, El-Habashy D, Elhalawani H, Fuller CD, Jreige M, Khamis Y, La Greca A, Mohamed A, Naser M, Prior JO, Ruan S, Tanadini-Lang S, Tankyevych O, Salimi Y, Vallières M, Vera P, Visvikis D, Wahid K, Zaidi H, Hatt M, Depeursinge A. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. HEAD AND NECK TUMOR SEGMENTATION AND OUTCOME PREDICTION : THIRD CHALLENGE, HECKTOR 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE, SEPTEMBER 22, 2022, PROCEEDINGS. HEAD AND NECK TUMOR SEGMENTATION CHALLENGE (3RD : 2022 : SINGAPOR... 2023; 13626:1-30. [PMID: 37195050 PMCID: PMC10171217 DOI: 10.1007/978-3-031-27420-6_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. Task 2 is the fully automatic prediction of Recurrence-Free Survival (RFS) from the same FDG-PET/CT and clinical data. The data were collected from nine centers for a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (DSCagg) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.
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Affiliation(s)
- Vincent Andrearczyk
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Valentin Oreiller
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Moamen Abobakr
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Leo Capriotti
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | - Joel Castelli
- Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France
- INSERM, U1099, Rennes, France
- University of Rennes 1, LTSI, Rennes, France
| | - Catherine Cheze Le Rest
- Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Pierre Decazes
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | - Ricardo Correia
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Dina El-Habashy
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Hesham Elhalawani
- Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, USA
| | | | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Yornna Khamis
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Mohamed Naser
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Su Ruan
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | | | - Olena Tankyevych
- Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pierre Vera
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | | | - Kareem Wahid
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Habib Zaidi
- Geneva University Hospital, Geneva, Switzerland
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Adrien Depeursinge
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
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de Oliveira ÍAF, Siero JCW, Dumoulin SO, van der Zwaag W. Improved Selectivity in 7 T Digit Mapping Using VASO-CBV. Brain Topogr 2023; 36:23-31. [PMID: 36517699 PMCID: PMC9834127 DOI: 10.1007/s10548-022-00932-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD.
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Affiliation(s)
- Ícaro A. F. de Oliveira
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Jeroen C. W. Siero
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.7692.a0000000090126352Radiology, Utrecht Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Serge O. Dumoulin
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands ,grid.5477.10000000120346234Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Wietske van der Zwaag
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
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25
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Zhu L, Gao G, Zhu Y, Han C, Liu X, Li D, Liu W, Wang X, Zhang J, Zhang X, Wang X. Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network. Front Oncol 2022; 12:958065. [PMID: 36249048 PMCID: PMC9558117 DOI: 10.3389/fonc.2022.958065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a cascaded deep learning model trained with apparent diffusion coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and localization of clinically significant prostate cancer (csPCa). Methods This retrospective study included 347 consecutive patients (235 csPCa, 112 non-csPCa) with high-quality prostate MRI data, which were randomly selected for training, validation, and testing. The ground truth was obtained using manual csPCa lesion segmentation, according to pathological results. The proposed cascaded model based on Res-UNet takes prostate MR images (T2WI+ADC or only ADC) as inputs and automatically segments the whole prostate gland, the anatomic zones, and the csPCa region step by step. The performance of the models was evaluated and compared with PI-RADS (version 2.1) assessment using sensitivity, specificity, accuracy, and Dice similarity coefficient (DSC) in the held-out test set. Results In the test set, the per-lesion sensitivity of the biparametric (ADC + T2WI) model, ADC model, and PI-RADS assessment were 95.5% (84/88), 94.3% (83/88), and 94.3% (83/88) respectively (all p > 0.05). Additionally, the mean DSC based on the csPCa lesions were 0.64 ± 0.24 and 0.66 ± 0.23 for the biparametric model and ADC model, respectively. The sensitivity, specificity, and accuracy of the biparametric model were 95.6% (108/113), 91.5% (665/727), and 92.0% (773/840) based on sextant, and were 98.6% (68/69), 64.8% (46/71), and 81.4% (114/140) based on patients. The biparametric model had a similar performance to PI-RADS assessment (p > 0.05) and had higher specificity than the ADC model (86.8% [631/727], p< 0.001) based on sextant. Conclusion The cascaded deep learning model trained with ADC and T2WI achieves good performance for automated csPCa detection and localization.
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Affiliation(s)
- Lina Zhu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yi Zhu
- Department of Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Chao Han
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Derun Li
- Department of Urology, Peking University First Hospital, Beijing, China
| | - Weipeng Liu
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Jingyuan Zhang
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
- *Correspondence: Xiaoying Wang,
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26
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A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain. PLoS One 2022; 17:e0274212. [PMID: 36067136 PMCID: PMC9447923 DOI: 10.1371/journal.pone.0274212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 08/23/2022] [Indexed: 11/20/2022] Open
Abstract
Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.
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Sharrock MF, Mould WA, Hildreth M, Ryu EP, Walborn N, Awad IA, Hanley DF, Muschelli J. Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume. J Neuroimaging 2022; 32:968-976. [PMID: 35434846 PMCID: PMC9474710 DOI: 10.1111/jon.12997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) clinical trials rely on manual linear and semi-quantitative (LSQ) estimators like the ABC/2, modified Graeb and IVH scores for timely volumetric estimation from CT. Deep learning (DL) volumetrics of ICH have recently approached the accuracy of gold-standard planimetry. However, DL and LSQ strategies have been limited by unquantified uncertainty, in particular when ICH and IVH estimates intersect. Bayesian deep learning methods can be used to approximate uncertainty, presenting an opportunity to improve quality assurance in clinical trials. METHODS A DL model was trained to simultaneously segment ICH and IVH using diagnostic CT data from the Minimally Invasive Surgery Plus Alteplase for ICH Evacuation (MISTIE) III and Clot Lysis: Evaluating Accelerated Resolution of IVH (CLEAR) III clinical trials. Bayesian uncertainty approximation was performed using Monte-Carlo dropout. We compared the performance of our model with estimators used in the CLEAR IVH and MISTIE II trials. The reliability of planimetry, DL, and LSQ volumetrics in the setting of high ICH and IVH intersection is quantified using consensus estimates. RESULTS Our DL model produced volume correlations and median Dice scores of .994 and .946 for ICH in MISTIE II, and .980 and .863 for IVH in CLEAR IVH, respectively, outperforming LSQ estimates from the clinical trials. We found significant linear relationships between ICH uncertainty, Dice scores (r = -.849), and relative volume difference (r = .735). CONCLUSION In our validation clinical trial dataset, DL models with Bayesian uncertainty approximation provided superior volumetric estimates to LSQ methods with real-time estimates of model uncertainty.
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Affiliation(s)
- Matthew F. Sharrock
- Division of Neurocritical Care, Department of Neurology, University of North Carolina at Chapel Hill, NC, USA
| | - W. Andrew Mould
- Division of Brain Injury Outcomes, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Meghan Hildreth
- Division of Brain Injury Outcomes, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - E. Paul Ryu
- Division of Brain Injury Outcomes, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Nathan Walborn
- Division of Brain Injury Outcomes, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Issam A. Awad
- Neurovascular Surgery Program, Section of Neurosurgery, Department of Surgery, University of Chicago Medicine and Biological Sciences, Chicago, IL, USA
| | - Daniel F. Hanley
- Division of Brain Injury Outcomes, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Huang L, Cai Y, Zhao E, Zhang S, Shu Y, Fan J. Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis. Scientometrics 2022. [DOI: 10.1007/s11192-022-04401-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Hutter MC. Differential Multimolecule Fingerprint for Similarity Search─Making Use of Active and Inactive Compound Sets in Virtual Screening. J Chem Inf Model 2022; 62:2726-2736. [PMID: 35613341 DOI: 10.1021/acs.jcim.2c00242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In conventional fingerprint methods, the similarity between two molecules is calculated using the Tanimoto index as a numerical criterion. Thus, the query molecules in virtual screening should be most representative of the wanted compound class at hand. In the concept introduced here, all available active molecules form a multimolecule fingerprint in which the appearing features are weighted according to their respective frequency. The features of inactive molecules are treated likewise and the resulting values are subtracted from those of the active ones. The obtained differential multimolecule fingerprint (DMMFP) is thus specific for the respective class of compounds. To account for the noninteger representation within this fingerprint, a modified Sørensen-Dice coefficient is used to compute the similarity. Potentially active molecules yield positive scores, whereas presumably inactive ones are denoted by negative values. The concept was applied to Angiotensin-converting enzyme (ACE) inhibitors, β2-adrenoceptor ligands, leukotriene A4 hydrolase inhibitors, dopamine D3 antagonists, and cytochrome CYP2C9 substrates, for which experimental binding affinities are known and was tested against decoys from DUD-E and a further background database consisting of molecules from the dark chemical matter, which comprises compounds that appear as frequent hitters across multiple assays. Using the 166 publicly available keys of the MACCS fingerprint and the larger PubChem fingerprint, actives were recovered with very high sensitivity. Furthermore, three marketed ACE inhibitors as well as the carbonic anhydrase II inhibitor dorzolamide were detected in the dark chemical matter data set. For comparison, the DMMFP was also used with a Bayesian classifier, for which the specificity (correctly classified inactives) and likewise the accuracy was superior. Conversely, the similarity score produced by the Sørensen-Dice coefficient showed its potential for the early recognition of (potentially) active molecules.
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Affiliation(s)
- Michael C Hutter
- Center for Bioinformatics, Saarland University, Campus E2.1, 66123 Saarbruecken, Germany
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Causey J, Stubblefield J, Qualls J, Fowler J, Cai L, Walker K, Guan Y, Huang X. An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1387-1392. [PMID: 34061750 PMCID: PMC9210325 DOI: 10.1109/tcbb.2021.3085608] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We present here the Arkansas AI-Campus solution method for the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Our Arkansas AI-Campus team participated the KiTS19 Challenge for four months, from March to July of 2019. This paper provides a summary of our methods, training, testing and validation results for this grand challenge in biomedical imaging analysis. Our deep learning model is an ensemble of U-Net models developed after testing many model variations. Our model has consistent performance on the local test dataset and the final competition independent test dataset. The model achieved local test Dice scores of 0.949 for kidney and tumor segmentation, and 0.601 for tumor segmentation, and the final competition test earned Dice scores 0.9470 and 0.6099 respectively. The Arkansas AI-Campus team solution with a composite DICE score of 0.7784 has achieved a final ranking of top fifty worldwide, and top five among the United States teams in the KiTS19 Competition.
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Stella M, Braat AJAT, Lam MGEH, de Jong HWAM, van Rooij R. 166Holmium- 99mTechnetium dual-isotope imaging: scatter compensation and automatic healthy-liver segmentation for 166Holmium radioembolization dosimetry. EJNMMI Phys 2022; 9:30. [PMID: 35445948 PMCID: PMC9023629 DOI: 10.1186/s40658-022-00459-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/08/2022] [Indexed: 11/21/2022] Open
Abstract
Background Partition modeling allows personalized activity calculation for holmium-166 (166Ho) radioembolization. However, it requires the definition of tumor and non-tumorous liver, by segmentation and registration of a separately acquired CT, which is time-consuming and prone to error. A protocol including 166Ho-scout, for treatment simulation, and technetium-99m (99mTc) stannous phytate for healthy-liver delineation was proposed. This study assessed the accuracy of automatic healthy-liver segmentation using 99mTc images derived from a phantom experiment. In addition, together with data from a patient study, the effect of different 99mTc activities on the 166Ho-scout images was investigated. To reproduce a typical scout procedure, the liver compartment, including two tumors, of an anthropomorphic phantom was filled with 250 MBq of 166Ho-chloride, with a tumor to non-tumorous liver activity concentration ratio of 10. Eight SPECT/CT scans were acquired, with varying levels of 99mTc added to the non-tumorous liver compartment (ranging from 25 to 126 MBq). For comparison, forty-two scans were performed in presence of only 99mTc from 8 to 240 MBq. 99mTc image quality was assessed by cold-sphere (tumor) contrast recovery coefficients. Automatic healthy-liver segmentation, obtained by thresholding 99mTc images, was evaluated by recovered volume and Sørensen–Dice index. The impact of 99mTc on 166Ho images and the role of the downscatter correction were evaluated on phantom scans and twenty-six patients’ scans by considering the reconstructed 166Ho count density in the healthy-liver. Results All 99mTc image reconstructions were found to be independent of the 166Ho activity present during the acquisition. In addition, cold-sphere contrast recovery coefficients were independent of 99mTc activity. The segmented healthy-liver volume was recovered fully, independent of 99mTc activity as well. The reconstructed 166Ho count density was not influenced by 99mTc activity, as long as an adequate downscatter correction was applied. Conclusion The 99mTc image reconstructions of the phantom scans all performed equally well for the purpose of automatic healthy-liver segmentation, for activities down to 8 MBq. Furthermore, 99mTc could be injected up to at least 126 MBq without compromising 166Ho image quality. Clinical trials The clinical study mentioned is registered with Clinicaltrials.gov (NCT02067988) on February 20, 2014.
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Affiliation(s)
- Martina Stella
- Department of Radiology and Nuclear Medicine, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
| | - Arthur J A T Braat
- Department of Radiology and Nuclear Medicine, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Marnix G E H Lam
- Department of Radiology and Nuclear Medicine, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Hugo W A M de Jong
- Department of Radiology and Nuclear Medicine, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Rob van Rooij
- Department of Radiology and Nuclear Medicine, UMC Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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Swartz JE, Smits HJG, Philippens MEP, de Bree R, H A M Kaanders J, Willems SM. Correlation and colocalization of HIF-1α and pimonidazole staining for hypoxia in laryngeal squamous cell carcinomas: A digital, single-cell-based analysis. Oral Oncol 2022; 128:105862. [PMID: 35447566 DOI: 10.1016/j.oraloncology.2022.105862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Tumor hypoxia results in worse local control and patient survival. We performed a digital, single-cell-based analysis to compare two biomarkers for hypoxia (hypoxia-inducible factor 1-alpha [HIF-1α] and pimonidazole [PIMO]) and their effect on outcome in laryngeal cancer patients treated with accelerated radiotherapy with or without carbogen breathing and nicotinamide (AR versus ARCON). MATERIALS AND METHODS Immunohistochemical staining was performed for HIF-1α and PIMO in consecutive sections of 44 laryngeal cancer patients randomized between AR and ARCON. HIF-1α expression and PIMO-binding were correlated using digital image analysis in QuPath. High-density areas for each biomarker were automatically annotated and staining overlap was analyzed. Kaplan-Meier survival analyses for local control, regional control and disease-free survival were performed to predict a response benefit of ARCON over AR alone for each biomarker. RESULTS 106 Tissue fragments of 44 patients were analyzed. A weak, significant positive correlation was observed between HIF-1α and PIMO positivity on fragment level, but not on patient level. A moderate strength correlation (r = 0.705, p < 0.001) was observed between the number of high-density staining areas for both biomarkers. Staining overlap was poor. HIF-1α expression, PIMO-binding or a combination could not predict a response benefit of ARCON over AR. CONCLUSION Digital image analysis to compare positive cell fractions and staining overlap between two hypoxia biomarkers using open-source software is feasible. Our results highlight that there are distinct differences between HIF-1α and PIMO as hypoxia biomarkers and therefore suggest co-existence of different forms of hypoxia within a single tumor.
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Affiliation(s)
- Justin E Swartz
- Department of Otorhinolaryngology - Head and Neck Surgery, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Hilde J G Smits
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Johannes H A M Kaanders
- Department of Radiation Oncology, University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
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Huang C, Xu Z, Shen Z, Luo T, Li T, Nissman D, Nelson A, Golightly Y, Niethammer M, Zhu H. DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative. Med Image Anal 2022; 77:102343. [PMID: 35026528 PMCID: PMC8901568 DOI: 10.1016/j.media.2021.102343] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/27/2021] [Accepted: 12/27/2021] [Indexed: 10/19/2022]
Abstract
Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.
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Affiliation(s)
- Chao Huang
- Department of Statistics, Florida State University, Tallahassee, 32304, USA
| | - Zhenlin Xu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Zhengyang Shen
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Daniel Nissman
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Amanda Nelson
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Yvonne Golightly
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
| | - Marc Niethammer
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA.
| | - Hongtu Zhu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA.
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Study on Body Size Measurement Method of Goat and Cattle under Different Background Based on Deep Learning. ELECTRONICS 2022. [DOI: 10.3390/electronics11070993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The feasibility of using depth sensors to measure the body size of livestock has been extensively tested. Most existing methods are only capable of measuring the body size of specific livestock in a specific background. In this study, we proposed a unique method of livestock body size measurement using deep learning. By training the data of cattle and goat with same feature points, different animal sizes can be measured under different backgrounds. First, a novel penalty function and an autoregressive model were introduced to reconstruct the depth image with super-resolution, and the effect of distance and illumination on the depth image was reduced. Second, under the U-Net neural network, the characteristics exhibited by the attention module and the DropBlock were adopted to improve the robustness of the background and trunk segmentation. Lastly, this study initially exploited the idea of human joint point location to accurately locate the livestock body feature points, and the livestock was accurately measured. According to the results, the average accuracy of this method was 93.59%. The correct key points for detecting the points of withers, shoulder points, shallowest part of the chest, highest point of the hip bones and ischia tuberosity had the percentages of 96.7%, 89.3%, 95.6%, 90.5% and 94.5%, respectively. In addition, the mean relative errors of withers height, hip height, body length and chest depth were only 1.86%, 2.07%, 2.42% and 2.72%, respectively.
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Di Sciacca G, Maffeis G, Farina A, Dalla Mora A, Pifferi A, Taroni P, Arridge S. Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210385GRR. [PMID: 35332743 PMCID: PMC8943242 DOI: 10.1117/1.jbo.27.3.036003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/28/2022] [Indexed: 06/01/2023]
Abstract
SIGNIFICANCE Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. AIM To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. APPROACH A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. RESULTS The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. CONCLUSIONS A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.
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Affiliation(s)
- Giuseppe Di Sciacca
- University College London, Department of Computer Science, London, United Kingdom
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
| | - Giulia Maffeis
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
| | - Andrea Farina
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | | | - Antonio Pifferi
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Paola Taroni
- Politecnico di Milano, Dipartimento di Fisica, Milano, Italy
- Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Milano, Italy
| | - Simon Arridge
- University College London, Department of Computer Science, London, United Kingdom
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Jin J, Zhang L, Leng E, Metzger GJ, Koopmeiners JS. Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data. Stat Med 2022; 41:483-499. [PMID: 34747059 PMCID: PMC9316890 DOI: 10.1002/sim.9245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 10/15/2021] [Accepted: 10/15/2021] [Indexed: 11/09/2022]
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.
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Affiliation(s)
- Jin Jin
- Division of Biostatistics, School of Public Health,
University of Minnesota, Minneapolis, MN 55455, USA
| | - Lin Zhang
- Division of Biostatistics, School of Public Health,
University of Minnesota, Minneapolis, MN 55455, USA
| | - Ethan Leng
- Department of Biomedical Engineering, University of
Minnesota, Minneapolis, MN 55455, USA
| | - Gregory J. Metzger
- Department of Radiology, Center for Magnetic Resonance
Research, University of Minnesota, Minneapolis, MN 55455, USA
| | - Joseph S. Koopmeiners
- Division of Biostatistics, School of Public Health,
University of Minnesota, Minneapolis, MN 55455, USA
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Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The volume estimation of retroperitoneal sarcoma (RPS) is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. This study aimed to evaluate two fully automated deep learning networks (ENet and ERFNet) for RPS segmentation. This retrospective study included 20 patients with RPS who received an abdominal computed tomography (CT) examination. Forty-nine CT examinations, with a total of 72 lesions, were included. Manual segmentation was performed by two radiologists in consensus, and automatic segmentation was performed using ENet and ERFNet. Significant differences between manual and automatic segmentation were tested using the analysis of variance (ANOVA). A set of performance indicators for the shape comparison (namely sensitivity), positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric differences (VD) were calculated. There were no significant differences found between the RPS volumes obtained using manual segmentation and ENet (p-value = 0.935), manual segmentation and ERFNet (p-value = 0.544), or ENet and ERFNet (p-value = 0.119). The sensitivity, PPV, DSC, VOE, and VD for ENet and ERFNet were 91.54% and 72.21%, 89.85% and 87.00%, 90.52% and 74.85%, 16.87% and 36.85%, and 2.11% and -14.80%, respectively. By using a dedicated GPU, ENet took around 15 s for segmentation versus 13 s for ERFNet. In the case of CPU, ENet took around 2 min versus 1 min for ERFNet. The manual approach required approximately one hour per segmentation. In conclusion, fully automatic deep learning networks are reliable methods for RPS volume assessment. ENet performs better than ERFNet for automatic segmentation, though it requires more time.
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Metallic Component Preserving Algorithm Based on the Cerebral Computed Tomography Angiography in Aneurysm Surgery. Diagnostics (Basel) 2022; 12:diagnostics12020338. [PMID: 35204429 PMCID: PMC8871085 DOI: 10.3390/diagnostics12020338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 02/01/2023] Open
Abstract
The purpose of this study was to investigate the viability of the proposed method in preventing the loss of metallic components including the clip and coil in cerebral computed tomography angiography (CTA). Forty patients undergoing surgery for aneurysms carried metallic materials. The proposed method is based on conventional bone subtraction CTA (BS-CTA) system. Briefly, the position of metal components was determined using the threshold value and a region of interest (ROI). An appropriate threshold was used to separate the background from the target materials based on the Otsu method. A three-dimensional (3D) rendering was performed from the proposed BS-CTA data carrying the extracted target information. The accuracy of clip and coil region measured using the dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) is reported. The metallic components of the proposed BS-CTA were significantly visualized in various patient cases. Quantitative evaluation using the proposed method is based on the mean DSC of 0.93 with a standard deviation (SD) of ±0.05 (e.g., maximum value = 0.99, minimum value = 0.75, 95% confidence interval (CI) = 0.91 to 0.95, and all p < 0.05). The mean HD was 1.50 voxels with an SD of ± 0.58 (e.g., maximum value = 5.95, minimum value = 0.12, 95% CI = 1.10 to 1.90, and all p < 0.05). The proposed method demonstrates effective segmentation of the metallic component and application to the existing conventional BS-CTA system.
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Jumutc V, Bļizņuks D, Lihachev A. Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation. SENSORS (BASEL, SWITZERLAND) 2022; 22:990. [PMID: 35161735 PMCID: PMC8839202 DOI: 10.3390/s22030990] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/17/2022] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder-decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of 0.809 was achieved for the foreground class.
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Affiliation(s)
- Vilen Jumutc
- Institute of Smart Computer Technologies, Riga Technical University, LV-1658 Riga, Latvia;
| | - Dmitrijs Bļizņuks
- Institute of Smart Computer Technologies, Riga Technical University, LV-1658 Riga, Latvia;
| | - Alexey Lihachev
- Institute of Atomic Physics and Spectroscopy, University of Latvia, LV-1586 Riga, Latvia;
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40
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Wulms N, Redmann L, Herpertz C, Bonberg N, Berger K, Sundermann B, Minnerup H. The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA. Front Aging Neurosci 2022; 13:720636. [PMID: 35126084 PMCID: PMC8812526 DOI: 10.3389/fnagi.2021.720636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 11/29/2021] [Indexed: 12/01/2022] Open
Abstract
Introduction: White matter hyperintensities of presumed vascular origin (WMH) are an important magnetic resonance imaging marker of cerebral small vessel disease and are associated with cognitive decline, stroke, and mortality. Their relevance in healthy individuals, however, is less clear. This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population. Methods: We evaluated the effect of varying training sample sizes on the accuracy and the robustness of the predicted white matter hyperintensity volume in a population (n = 201) with a low prevalence of confluent WMH and a substantial proportion of participants without WMH. BIANCA was trained with seven different sample sizes between 10 and 40 with increments of 5. For each sample size, 100 random samples of T1w and FLAIR images were drawn and trained with manually delineated masks. For validation, we defined an internal and external validation set and compared the mean absolute error, resulting from the difference between manually delineated and predicted WMH volumes for each set. For spatial overlap, we calculated the Dice similarity index (SI) for the external validation cohort. Results: The study population had a median WMH volume of 0.34 ml (IQR of 1.6 ml) and included n = 28 (18%) participants without any WMH. The mean absolute error of the difference between BIANCA prediction and manually delineated masks was minimized and became more robust with an increasing number of training participants. The lowest mean absolute error of 0.05 ml (SD of 0.24 ml) was identified in the external validation set with a training sample size of 35. Compared to the volumetric overlap, the spatial overlap was poor with an average Dice similarity index of 0.14 (SD 0.16) in the external cohort, driven by subjects with very low lesion volumes. Discussion: We found that the performance of BIANCA, particularly the robustness of predictions, could be optimized for use in populations with a low WMH load by enlargement of the training sample size. Further work is needed to evaluate and potentially improve the prediction accuracy for low lesion volumes. These findings are important for current and future population-based studies with the majority of participants being normal aging people.
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Affiliation(s)
- Niklas Wulms
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
- *Correspondence: Niklas Wulms
| | - Lea Redmann
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Christine Herpertz
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Nadine Bonberg
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
| | - Benedikt Sundermann
- Clinic of Radiology, University Hospital Muenster, Muenster, Germany
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, Medical Campus, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Heike Minnerup
- Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany
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41
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Dey N, V. R. Introduction to image-assisted disease screening. Magn Reson Imaging 2022. [DOI: 10.1016/b978-0-12-823401-3.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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42
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Xin B, Huang J, Zhang L, Zheng C, Zhou Y, Lu J, Wang X. Dynamic topology analysis for spatial patterns of multifocal lesions on MRI. Med Image Anal 2021; 76:102267. [PMID: 34929461 DOI: 10.1016/j.media.2021.102267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 07/26/2021] [Accepted: 10/07/2021] [Indexed: 01/01/2023]
Abstract
Quantitatively analysing the spatial patterns of multifocal lesions on clinical MRI is an important step towards a better understanding of the disease and for precision medicine, which is yet to be properly explored by feature engineering and deep learning methods. Network science addresses this issue by explicitly modeling the inter-lesion topology. However, the construction of the informative graph with optimal edge sparsity and quantification of community graph structures are the current challenges in network science. In this paper, we address these challenges with a novel Dynamic Topology Analysis framework on the basis of persistent homology, aiming to investigate the predictive values of global geometry and local clusters of multifocal lesions. Firstly, Dynamic Hierarchical Network is proposed to construct informative global and community-level topology over multi-scale networks from sparse to dense. Multi-scale global topology is constructed with a nested sequence of Rips complexes, from which a new K-simplex Filtration is designed to generate a higher-level topological abstraction for community identification based on the connectivity of k-simplices in the Rips Complex. Secondly, to quantify multi-scale community structures, we design a new Decomposed Community Persistence algorithm to track the dynamic evolution of communities, and then summarise the evolutionary communities incorporated with a customisable descriptor. The quantified community features are encapsulated with global geometric invariants for topological pattern analysis. The proposed framework was evaluated on both diagnostic differentiation and prognostic prediction for multiple sclerosis that is a typical multifocal disease, and achieved ROC_AUC 0.875 and 0.767, respectively, outperforming seven state-of-the-art persistent homology methods and the reported performance of six feature engineering and deep learning methods.
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Affiliation(s)
- Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Jing Huang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lin Zhang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Chaojie Zheng
- Central Research Institute, United Imaging Healthcare Group Co, Ltd, Shanghai, China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group Co, Ltd, Shanghai, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
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43
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Kumazu Y, Kobayashi N, Kitamura N, Rayan E, Neculoiu P, Misumi T, Hojo Y, Nakamura T, Kumamoto T, Kurahashi Y, Ishida Y, Masuda M, Shinohara H. Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy. Sci Rep 2021; 11:21198. [PMID: 34707141 PMCID: PMC8551298 DOI: 10.1038/s41598-021-00557-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/13/2021] [Indexed: 02/06/2023] Open
Abstract
The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons’ experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335–0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45–3.95). The mean misrecognition score was a low 0.14 (range 0–0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.
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Affiliation(s)
- Yuta Kumazu
- Department of Surgery, Yokohama City University, Kanagawa, Japan.,Anaut Inc., Tokyo, Japan
| | | | | | | | | | - Toshihiro Misumi
- Department of Biostatistics, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Yudai Hojo
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Tatsuro Nakamura
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Tsutomu Kumamoto
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Yasunori Kurahashi
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Yoshinori Ishida
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Munetaka Masuda
- Department of Surgery, Yokohama City University, Kanagawa, Japan
| | - Hisashi Shinohara
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
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44
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Delisle PL, Anctil-Robitaille B, Desrosiers C, Lombaert H. Realistic image normalization for multi-Domain segmentation. Med Image Anal 2021; 74:102191. [PMID: 34509168 DOI: 10.1016/j.media.2021.102191] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 06/22/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022]
Abstract
Image normalization is a building block in medical image analysis. Conventional approaches are customarily employed on a per-dataset basis. This strategy, however, prevents the current normalization algorithms from fully exploiting the complex joint information available across multiple datasets. Consequently, ignoring such joint information has a direct impact on the processing of segmentation algorithms. This paper proposes to revisit the conventional image normalization approach by, instead, learning a common normalizing function across multiple datasets. Jointly normalizing multiple datasets is shown to yield consistent normalized images as well as an improved image segmentation when intensity shifts are large. To do so, a fully automated adversarial and task-driven normalization approach is employed as it facilitates the training of realistic and interpretable images while keeping performance on par with the state-of-the-art. The adversarial training of our network aims at finding the optimal transfer function to improve both, jointly, the segmentation accuracy and the generation of realistic images. We have evaluated the performance of our normalizer on both infant and adult brain images from the iSEG, MRBrainS and ABIDE datasets. The results indicate that our contribution does provide an improved realism to the normalized images, while retaining a segmentation accuracy at par with the state-of-the-art learnable normalization approaches.
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Affiliation(s)
| | | | | | - Herve Lombaert
- Department of Computer and Software Engineering, ETS Montreal, Canada
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45
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A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors. SENSORS 2021; 21:s21144928. [PMID: 34300667 PMCID: PMC8309718 DOI: 10.3390/s21144928] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/09/2021] [Accepted: 07/17/2021] [Indexed: 11/16/2022]
Abstract
Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.
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46
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Saha A, Hosseinzadeh M, Huisman H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal 2021; 73:102155. [PMID: 34245943 DOI: 10.1016/j.media.2021.102155] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/30/2021] [Accepted: 06/08/2021] [Indexed: 01/22/2023]
Abstract
We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model2 for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive its detection network, targeting salient structures and highly discriminative feature dimensions across multiple resolutions. Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. Simultaneously, a decoupled residual classifier is used to achieve consistent false positive reduction, without sacrificing high sensitivity or computational efficiency. In order to guide model generalization with domain-specific clinical knowledge, a probabilistic anatomical prior is used to encode the spatial prevalence and zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort. For 486 institutional testing scans, the 3D CAD system achieves 83.69±5.22% and 93.19±2.96% detection sensitivity at 0.50 and 1.46 false positive(s) per patient, respectively, with 0.882±0.030 AUROC in patient-based diagnosis -significantly outperforming four state-of-the-art baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from recent literature. For 296 external biopsy-confirmed testing scans, the ensembled CAD system shares moderate agreement with a consensus of expert radiologists (76.69%; kappa = 0.51±0.04) and independent pathologists (81.08%; kappa = 0.56±0.06); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.
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Affiliation(s)
- Anindo Saha
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands.
| | - Matin Hosseinzadeh
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands
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47
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Alksas A, Shehata M, Saleh GA, Shaffie A, Soliman A, Ghazal M, Khelifi A, Khalifeh HA, Razek AA, Giridharan GA, El-Baz A. A novel computer-aided diagnostic system for accurate detection and grading of liver tumors. Sci Rep 2021; 11:13148. [PMID: 34162893 PMCID: PMC8222341 DOI: 10.1038/s41598-021-91634-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 05/28/2021] [Indexed: 12/13/2022] Open
Abstract
Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F\documentclass[12pt]{minimal}
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\begin{document}$$_{1}$$\end{document}1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of \documentclass[12pt]{minimal}
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\begin{document}$$88\%\pm 5\%$$\end{document}88%±5%, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.
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Affiliation(s)
- Ahmed Alksas
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohamed Shehata
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Gehad A Saleh
- Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura, 35516, Egypt
| | - Ahmed Shaffie
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Soliman
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Mohammed Ghazal
- College of Engineering, Abu Dhabi University, Abu Dhabi, UAE
| | - Adel Khelifi
- Computer Science and Information Technology, Abu Dhabi University, Abu Dhabi, UAE
| | | | - Ahmed Abdel Razek
- Department of Radiology, Faculty of Medicine, Mansoura University, Mansoura, 35516, Egypt
| | - Guruprasad A Giridharan
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA
| | - Ayman El-Baz
- BioImaging Lab, Department of Bioengineering, University of Louisville, Louisville, KY, 40292, USA.
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48
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Xu H, Langer M, Peyrin F. Quantitative analysis of bone microvasculature in a mouse model using the monogenic signal phase asymmetry and marker-controlled watershed. Phys Med Biol 2021; 66. [PMID: 34030142 DOI: 10.1088/1361-6560/ac047d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/24/2021] [Indexed: 12/24/2022]
Abstract
Thethree-dimensional (3D) imaging and quantitative analysis of bone microvasculature are important to describe angiogenesis involvement in bone metastatic processes. Here, we propose an algorithm based on marker-controlled watershed for the 3D segmentation of vessels and bone in mouse bone imaged with a contrast agent using synchrotron radiation micro-computed tomography (SR-μCT). Markers were generated using hysteresis thresholding and morphological filters, and the control surface was constructed using the monogenic signal phase asymmetry. The accuracy and robustness of the proposed method were evaluated on a series of synthetic volumes generated to mimic the vessel, bone and background structures. Different contrast between different structures, as well as different noise levels were considered. A series of multi-class synthetic volumes were segmented using the proposed method, and the overall segmentation quality was evaluated using the Matthews correlation coefficient (MCC) by comparing to the ground truth. Additionally, we evaluated the segmentation of thin structures under various levels of Gaussian noise. The simulation study indicated that the algorithm was performant in multi-class segmentation with different contrast, noise, and thickness. The algorithm was applied to images of bone from a mouse model of breast cancer bone metastasis acquired using SR-μCT. The segmentation quality was evaluated using the Dice coefficient and the MCC by comparing to manual segmentation. The proposed method performed better than hysteresis thresholding and marker-controlled watershed using the magnitude of the gradient as control surface. Several quantitative parameters on bone and vessels were extracted, including bone volume fraction (BV/TV), vessel volume fraction (VV/TV) and the mean vessel thickness (VTh). The bone volume fraction (BV/TV) was significantly lower in the metastatic group compared to the healthy group. This demonstrated the effectiveness of the algorithm for the study of bone and vessel microstructures in mouse model.
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Affiliation(s)
- Hao Xu
- Creatis, Université de Lyon, CNRS UMR5220, Inserm U1206, Université Lyon 1, INSA-Lyon, Villeurbanne, France
| | - Max Langer
- Creatis, Université de Lyon, CNRS UMR5220, Inserm U1206, Université Lyon 1, INSA-Lyon, Villeurbanne, France
| | - Françoise Peyrin
- Creatis, Université de Lyon, CNRS UMR5220, Inserm U1206, Université Lyon 1, INSA-Lyon, Villeurbanne, France.,European Synchrotron Radiation Facility (ESRF), Grenoble, France
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49
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Waisman A, Norris AM, Elías Costa M, Kopinke D. Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle. Sci Rep 2021; 11:11793. [PMID: 34083673 PMCID: PMC8175575 DOI: 10.1038/s41598-021-91191-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/24/2021] [Indexed: 12/05/2022] Open
Abstract
Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.
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Affiliation(s)
- Ariel Waisman
- CONICET - Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (FLENI), Laboratorio de Investigación Aplicada a Neurociencias (LIAN), Buenos Aires, Argentina.
| | - Alessandra Marie Norris
- Department of Pharmacology and Therapeutics, University of Florida College of Medicine, Gainesville, 32610, FL, USA.,Myology Institute, University of Florida College of Medicine, Gainesville, FL, USA
| | | | - Daniel Kopinke
- Department of Pharmacology and Therapeutics, University of Florida College of Medicine, Gainesville, 32610, FL, USA. .,Myology Institute, University of Florida College of Medicine, Gainesville, FL, USA.
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50
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Anagnostis A, Tagarakis AC, Kateris D, Moysiadis V, Sørensen CG, Pearson S, Bochtis D. Orchard Mapping with Deep Learning Semantic Segmentation. SENSORS 2021; 21:s21113813. [PMID: 34072975 PMCID: PMC8198531 DOI: 10.3390/s21113813] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/21/2021] [Accepted: 05/27/2021] [Indexed: 12/19/2022]
Abstract
This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.
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Affiliation(s)
- Athanasios Anagnostis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- Department of Computer Science & Telecommunications, University of Thessaly, GR35131 Lamia, Greece
| | - Aristotelis C. Tagarakis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
| | - Dimitrios Kateris
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- Correspondence: ; Tel.: +30-242-109-6740
| | - Vasileios Moysiadis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
| | - Claus Grøn Sørensen
- Department of Electrical and Computer Engineering, Aarhus University, DK-8000 Aarhus C, Denmark;
| | - Simon Pearson
- Lincoln Institute for Agri-Food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK;
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology–Hellas (CERTH), GR57001 Thessaloniki, Greece; (A.A.); (A.C.T.); (V.M.); (D.B.)
- farmB Digital Agriculture P.C., Doiranis 17, GR54639 Thessaloniki, Greece
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