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arXiv 2021年

作者： Vicario, Celia Martín Kordon, Florian Denzinger, Felix El Barbari, Jan Siad Privalov, Maxim Franke, Jochen Thomas, Sarina Kausch, Lisa Maier, Andreas Kunze, Holger Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany Siemens Healthcare GmbH Forchheim Germany Department for Trauma and Orthopaedic Surgery BG Trauma Center Ludwigshafen Ludwigshafen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany

Purpose 3D acquisitions are often acquired to assess the result in orthopedic trauma surgery. With a mobile C-Arm system, these acquisitions can be performed intra-operatively. That reduces the number of required revision surgeries. However, due to the operation room setup, the acquisitions typically cannot be performed such that the acquired volumes are aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. In this paper, we present a detailed study of multi-task learning (MTL) regression networks to estimate the parameters of the MPR planes. Approach First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, three different MTL network architectures based on the PoseNet are compared with a single task learning network. Results Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7◦ to 7.3◦ in the mean value for single anatomies. The multi-head approach improves the regression of the plane position from 7.4mm to 6.1mm, while the orientation does not benefit from this approach. Conclusions The results show that a multi-head approach can lead to slightly better results than the individual tasks networks. The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： image segmentation

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Generalizable Cross-modality medical image Segmentation via Style Augmentation and Dual Normalization

arXiv

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arXiv 2021年

作者： Zhou, Ziqi Qi, Lei Yang, Xin Ni, Dong Shi, Yinghuan The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University China The School of Computer Science and Engineering Southeast University China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center The Medical Ultrasound Image Computing Lab The Marshall Laboratory of Biomedical Engineering Shenzhen University China

For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain? This setting, namely generalizable cross-modality segmentation, owning its clinical potential, is much more challenging than other related settings, e.g., domain adaptation. To achieve this goal, we in this paper propose a novel dual-normalization model by leveraging the augmented source-similar and source-dissimilar images during our generalizable segmentation. To be specific, given a single source domain, aiming to simulate the possible appearance change in unseen target domains, we first utilize a nonlinear transformation to augment source-similar and source-dissimilar images. Then, to sufficiently exploit these two types of augmentations, our proposed dual-normalization based model employs a shared backbone yet independent batch normalization layer for separate normalization. Afterward, we put forward a style-based selection scheme to automatically choose the appropriate path in the test stage. Extensive experiments on three publicly available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal Multi-Organ datasets, have demonstrated that our method outperforms other state-of-the-art domain generalization methods. Code is available at https://***/zzzqzhou/Dual-Normalization. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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Retinal morphological differences in atypical Parkinsonism: A cross-sectional analysis of the AlzEye cohort

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Biomarkers in Neuropsychiatry 2024年 10卷 100096-100096页

作者： Patel, S. Bredemeyer, O. Williamson, D.J. Struyven, R.R. Zhou, Y. Denniston, A.K. Petzold, A. Antoniades, C.A. Keane, P.A. Wagner, S.K. NeuroMetrology Lab Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology London United Kingdom Institute of Ophthalmology University College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London United Kingdom University of Birmingham Birmingham United Kingdom University Hospitals Birmingham NHS Foundation Trust Birmingham United Kingdom NIHR Birmingham Biomedical Research Centre University of Birmingham Birmingham United Kingdom Queen Square Institute of Neurology University College London London United Kingdom

Objective: Atypical Parkinsonian syndrome (APS) describes a heterogeneous group of disorders mimicking the clinical presentation of Parkinson disease (PD) but with disparate natural history and pathophysiology. While retinal markers of PD are increasingly described, APS has been afforded less attention possibly owing to its lower prevalence. Here, we investigate retinal morphological differences in individuals with APS in a large real world cohort. Methods: We conducted a cross-sectional analysis of the AlzEye study, a retrospective cohort where ophthalmic data of individuals attending Moorfields Eye Hospital between January 2008 and March 31st 2018 (inclusive) has been linked with systemic disease data through national hospital admissions. Retinal features were extracted from macula-centered color fundus photography (CFP) and optical coherence tomography (OCT) and compared between individuals with APS and those unaffected. Individuals with idiopathic PD were excluded. Retinal neural and vascular features were measured using automated segmentation and analyzed with multivariable-adjusted regression models. Results: Among a cohort of 91,170 patients, there were 51 patients with APS and 91,119 controls. Individuals with APS were older and more likely to have hypertension and diabetes mellitus. After adjusting for age, sex, hypertension and diabetes melitus, individuals with APS had a thinner ganglion cell-inner plexiform layer (-3.95 microns, 95% CI: −7.53, −0.37, p = 0.031) but no difference in other retinoneural or retinovascular indices. Optic nerve cup-to-disc ratio was similar between groups. Conclusion: Our cross-sectional analysis of the AlzEye cohort reveals distinct retinal morphological characteristics in APS compared to healthy controls. The study notably identifies a thinner ganglion cell-inner plexiform layer in APS patients, without accompanying changes in the inner nuclear layer or significant alterations in retinovascular indices and optic nerve cup-di

关键词： AlzEye Atypical Parkinsonian disorders OCT Ocular Biomarker Retina

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Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance

arXiv

引用

arXiv 2023年

作者： Cheung, Wing Keung Pakzad, Ashkan Mogulkoc, Nesrin Needleman, Sarah Rangelov, Bojidar Gudmundsson, Eyjolfur Zhao, An Abbas, Mariam McLaverty, Davina Asimakopoulos, Dimitrios Chapman, Robert Savas, Recep Janes, Sam M. Hu, Yipeng Alexander, Daniel C. Hurst, John R. Jacob, Joseph Satsuma Lab Centre for Medical Image Computing University College London London United Kingdom Department of Computer Science University College London London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of Respiratory Medicine Ege University Hospital Izmir Turkey Medical School University College London London United Kingdom School of Clinical Medicine University of Cambridge Cambridge United Kingdom Interstitial Lung Disease Service Department of Respiratory Medicine University College London Hospitals NHS Foundation Trust London United Kingdom Department of Radiology Ege University Hospital Izmir Turkey Lungs for Living Research Centre UCL London United Kingdom UCL Respiratory University College London London United Kingdom Respiratory Medicine Royal Free London NHS Foundation Trust London United Kingdom

The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52% and 85.80%, (B) outperforms the baseline models by 2.89%, 3.86% and 3.87% on average and (C) produces maximum segmentation performance gain by 14.11%, 9.28% and 12.70% for individual case when (1) nnU-Net with instant normalisation and leaky ReLU, (2) nnU-Net with batch normalisation and ReLU and (3) modified dilated U-Net are used respectively. Furthermore, our proposed technique has a low RAM/GPU memory usage;and it is GPU memory efficient and highly flexible enabling it to be deployed on any 2D deep learning model. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Retinal image Classification via Vasculature-Guided Sequential Attention

Retinal Image Classification via Vasculature-Guided Sequenti...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Mengliu Zhao Ghassan Hamarneh Medical Image Analysis Lab School of Computing Science Simon Fraser University Canada

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Age-related macular degeneration and diabetic retinopathy are diseases of increasing prevalence globally in recent years. Traditionally, diagnosing these diseases relied on manual visual inspection by experts, which was costly, time-consuming and laborious as it required closely examining high-resolution color fundus images. More recently, deep learning networks have shown great potential in predicting diseases from retinal images. However, being purely data-driven, these networks are susceptible to overfitting and their training requires large annotated data. In this paper, we propose to enrich deep learning-based fundus image classifiers with prior knowledge on special structures in the retina implicated with the disease. In particular, we leverage vessel priors to guide the attention mechanism of deep learning architectures. In addition, we leverage a bi-directional dual-layer LSTM module to learn the inter-dependencies between a sequence of prior-guided attention maps deployed across the depth of the disease classification network. Results on the clinical datasets show the proposed method could bring performance improvement by as much as 8%?

关键词： Retina Retinopathy Machine learning Biomedical imaging Blood vessels Feature extraction

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A 3D deep learning classifier and its explainability when assessing coronary artery disease

arXiv

引用

arXiv 2023年

作者： Cheung, Wing Keung Kalindjian, Jeremy Bell, Robert Nair, Arjun Menezes, Leon J. Patel, Riyaz Wan, Simon Chou, Kacy Chen, Jiahang Torii, Ryo Davies, Rhodri H. Moon, James C. Alexander, Daniel C. Jacob, Joseph Satsuma Lab Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Hatter Cardiovascular Institute University College London London United Kingdom Department of Radiology University College London Hospital London United Kingdom Institute of Nuclear Medicine University College London London United Kingdom Institute of Cardiovascular Science Faculty of Population Health Sciences University College London London United Kingdom Department of Mechanical Engineering University College London London United Kingdom Barts Heart Centre West Smithfield London United Kingdom Department of Respiratory Medicine University College London London United Kingdom

Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analysing medical images from computed tomography coronary angiography (CTCA). Most current approaches utilise deep learning methods but require centerline extraction and multi-planar reconstruction. These indirect methods are not designed in a clinician-friendly manner, and they complicate the interventional procedure. Furthermore, the current deep learning methods do not provide exact explainability and limit the usefulness of these methods to be deployed in clinical settings. In this study, we first propose a 3D Resnet-50 deep learning model to directly classify normal subjects and CAD patients on CTCA images, then we demonstrate a 2D modified U-Net model can be subsequently employed to segment the coronary arteries. Our proposed approach outperforms the state-of-the-art models by 21.43% in terms of classification accuracy. The classification model with focal loss provides a better and more focused heat map, and the segmentation model provides better explainability than the classification-only model. The proposed holistic approach not only provides a simpler and clinician-friendly solution but also good classification accuracy and exact explainability for CAD diagnosis. © 2023, CC BY-NC-ND.

关键词： image segmentation

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Combination of global and nonlocal sparse regularization priors for MR image reconstruction

Combination of global and nonlocal sparse regularization pri...

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IEEE Region 10 International Conference TENCON

作者： Raji Susan Mathew Joseph Suresh Paul Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management Kerala

Magnetic resonance (MR) image recovery from the undersampled k-space data using fixed basis transforms such as wavelet utilizes the global sparsity of images. On the other hand, redundant representation schemes such as block-matching and 3D filtering (BM3D) make use of the the non-local sparsity of images, resulting in better preservation of finer details. But, use of any of these individual methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an algorithm for efficient combination of the global and non-local sparsity through the use of the fast composite splitting denoising (FCSD) algorithm. Here wavelets and BM3D are used for global and non-local sparsity, respectively. Additionally, we use adaptive iterative soft thresholding algorithm (ISTA) for wavelet recovery that uses an adaptive regularization parameter (threshold) in each iteration. The proposed adaptive fast composite splitting denoising (AFCSD) algorithm combines the merits of both BM3D with adaptive ISTA. This is because, it utilizes the self-repetitive structure in images by enforcing nonlocal sparsity as well as entire consistency of the image. The proposed method is superior to any of the individual methods in terms of reconstructed image quality, computation complexity and compute time. Furthermore, we show that the combined sparsity constraint can yield further improvements in reconstruction with significant reduction in the compute time when used as an auxiliary constraint in Dictionary Learning (DL), compared to a standard DL reconstruction.

关键词： image reconstruction Wavelet transforms Signal processing algorithms Convergence Three-dimensional displays Noise reduction

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering 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. It was developed by a large international consortium in a multi-stage Delphi process and 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), data set and algorithm output. Based on 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 a classification task 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. © 2022, CC BY.

关键词： Semantic Segmentation

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