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      The Cell Tracking Challenge: 10 years of objective benchmarking

      research-article
      1 , 1 , 2 , 3 , 4 , 3 , 4 , 5 , 1 , 6 , 7 , 6 , 8 , 9 , 9 , 9 , 10 , 10 , 10 , 11 , 12 , 12 , 12 , 12 , 12 , 1 , 1 , 13 , 14 , 15 , 16 , 16 , 17 , 17 , 17 , 18 , 19 , 19 , 20 , 18 , 21 , 22 , 23 , 24 , 24 , 22 , 7 , 3 , 4 , 1 , , 24 ,
      Nature Methods
      Nature Publishing Group US
      Image processing, Computational platforms and environments

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          Abstract

          The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

          Abstract

          This updated analysis of the Cell Tracking Challenge explores how algorithms for cell segmentation and tracking in both 2D and 3D have advanced in recent years, pointing users to high-performing tools and developers to open challenges.

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          Most cited references45

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

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              nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

              Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.
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                Author and article information

                Contributors
                kozubek@fi.muni.cz
                codesolorzano@unav.es
                Journal
                Nat Methods
                Nat Methods
                Nature Methods
                Nature Publishing Group US (New York )
                1548-7091
                1548-7105
                18 May 2023
                18 May 2023
                2023
                : 20
                : 7
                : 1010-1020
                Affiliations
                [1 ]GRID grid.10267.32, ISNI 0000 0001 2194 0956, Centre for Biomedical Image Analysis, Faculty of Informatics, , Masaryk University, ; Brno, Czech Republic
                [2 ]GRID grid.440850.d, ISNI 0000 0000 9643 2828, IT4Innovations National Supercomputing Center, , VSB – Technical University of Ostrava, ; Ostrava, Czech Republic
                [3 ]GRID grid.7840.b, ISNI 0000 0001 2168 9183, Bioengineering Department, , Universidad Carlos III de Madrid, ; Madrid, Spain
                [4 ]GRID grid.410526.4, ISNI 0000 0001 0277 7938, Instituto de Investigación Sanitaria Gregorio Marañón, ; Madrid, Spain
                [5 ]GRID grid.418346.c, ISNI 0000 0001 2191 3202, Optical Cell Biology, , Instituto Gulbenkian de Ciência, ; Oeiras, Portugal
                [6 ]GRID grid.411227.3, ISNI 0000 0001 0670 7996, Centro de Informatica, , Universidade Federal de Pernambuco, ; Recife, Brazil
                [7 ]GRID grid.20861.3d, ISNI 0000000107068890, Center for Advanced Methods in Biological Image Analysis, Beckman Institute, , California Institute of Technology, ; Pasadena, CA USA
                [8 ]GRID grid.20861.3d, ISNI 0000000107068890, Division of Biology and Biological Engineering and Howard Hughes Medical Institute, , California Institute of Technology, ; Pasadena, CA USA
                [9 ]GRID grid.7892.4, ISNI 0000 0001 0075 5874, Institute for Automation and Applied Informatics, , Karlsruhe Institute of Technology, ; Eggenstein-Leopoldshafen, Germany
                [10 ]GRID grid.169077.e, ISNI 0000 0004 1937 2197, The Elmore Family School of Electrical and Computer Engineering, , Purdue University, ; West Lafayette, IN USA
                [11 ]GRID grid.38142.3c, ISNI 000000041936754X, Boston Children’s Hospital and Harvard Medical School, ; Boston, MA USA
                [12 ]GRID grid.134936.a, ISNI 0000 0001 2162 3504, CIVA Lab, Department of Electrical Engineering and Computer Science, , University of Missouri, ; Columbia, MO USA
                [13 ]GRID grid.462143.6, ISNI 0000 0004 0382 6019, Institut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, ; Lyon, France
                [14 ]GRID grid.4444.0, ISNI 0000 0001 2112 9282, Centre National de la Recherche Scientifique (CNRS), ; Paris, France
                [15 ]GRID grid.509897.a, ISNI 0000 0004 0627 1151, Raysearch Laboratories AB, ; Stockholm, Sweden
                [16 ]GRID grid.166341.7, ISNI 0000 0001 2181 3113, Department of Electrical and Computer Engineering, , Drexel University, ; Philadelphia, PA USA
                [17 ]GRID grid.7489.2, ISNI 0000 0004 1937 0511, School of Electrical and Computer Engineering, , Ben-Gurion University of the Negev, ; Beersheba, Israel
                [18 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Division of Medical Image Computing, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [19 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Helmholtz Imaging, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [20 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Interactive Machine Learning Group, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [21 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Pattern Analysis and Learning Group, Department of Radiation Oncology, , Heidelberg University Hospital, ; Heidelberg, Germany
                [22 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, School of Computer Science and Engineering, , University of New South Wales, ; Sydney, New South Wales Australia
                [23 ]GRID grid.1022.1, ISNI 0000 0004 0437 5432, Griffith University, ; Nathan, Queensland Australia
                [24 ]GRID grid.5924.a, ISNI 0000000419370271, Biomedical Engineering Program and Ciberonc, Center for Applied Medical Research, , Universidad de Navarra, ; Pamplona, Spain
                Author information
                http://orcid.org/0000-0003-3544-6494
                http://orcid.org/0000-0002-4270-7982
                http://orcid.org/0000-0002-5991-7791
                http://orcid.org/0000-0003-2082-3277
                http://orcid.org/0000-0002-5896-2347
                http://orcid.org/0000-0002-9999-5321
                http://orcid.org/0000-0002-3677-0264
                http://orcid.org/0000-0003-4798-5153
                http://orcid.org/0000-0001-8755-2825
                http://orcid.org/0000-0001-9769-3517
                http://orcid.org/0000-0001-9100-5496
                http://orcid.org/0000-0002-1690-5897
                http://orcid.org/0000-0002-4961-8229
                http://orcid.org/0000-0003-1754-0823
                http://orcid.org/0000-0002-3707-8157
                http://orcid.org/0000-0003-4125-1597
                http://orcid.org/0000-0002-1392-9340
                http://orcid.org/0000-0002-5329-575X
                http://orcid.org/0000-0002-8238-8090
                http://orcid.org/0000-0002-2541-6024
                http://orcid.org/0000-0002-1573-1661
                http://orcid.org/0000-0001-7902-589X
                http://orcid.org/0000-0001-8720-0205
                Article
                1879
                10.1038/s41592-023-01879-y
                10333123
                37202537
                327197e3-b8c0-447d-a805-73e0c6d80be7
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 August 2022
                : 13 April 2023
                Funding
                Funded by: Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (MCIU/AEI/10.13039/50110011033) and FEDER funds UE under Grants number RTI2018-094494-B-C22, TED2021-131300B-I00 and PDI2021-122409OB-C22
                Funded by: Czech Ministry of Education, Youth and Sports national research infrastructure Czech-BioImaging grants number LM2018129 and CZ.02.1.01/0.0/0.0/18_046/0016045
                Funded by: FundRef https://doi.org/10.13039/501100008530, EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj);
                Award ID: CZ.02.1.01/0.0/0.0/16_013/0001791
                Award Recipient :
                Funded by: - Czech Ministry of Education, Youth and Sports national research infrastructure Czech-BioImaging grants number LM2018129 and CZ.02.1.01/0.0/0.0/18_046/0016045 - Czech Ministry of Education, Youth and Sports through the e-INFRA CZ project ID:90140
                Funded by: - Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, MCIN / AEI / 10.13039/501100011033/, co-financed by European Regional Development Fund (ERDF), “A way of making Europe” Grant number PID2019-109820RB-I00 - NVIDIA Corporation for the donation of the Titan X (Pascal) GPU
                Funded by: FundRef https://doi.org/10.13039/501100005635, Fundação Calouste Gulbenkian (Calouste Gulbenkian Foundation);
                Funded by: FundRef https://doi.org/10.13039/100004410, European Molecular Biology Organization (EMBO);
                Award ID: EMBO-2020-IG- 4734
                Award ID: EMBO ALTF 174-2022
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100005471, UofI | UIUC | Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign (Beckman Institute for Advanced Science and Technology);
                Funded by: - Brazilian funding agencies FACEPE, CAPES and CNPq - Beckman Institute at Caltech
                Funded by: Brazilian funding agencies FACEPE, CAPES and CNPq
                Funded by: FundRef https://doi.org/10.13039/100000011, Howard Hughes Medical Institute (HHMI);
                Funded by: FundRef https://doi.org/10.13039/501100009318, Helmholtz Association;
                Funded by: Cognitive Information Processing and Biointerfaces International Graduate School (BIF-IGS)
                Funded by: Helmholtz Information and Data Science School for Health (HIDSS4Health)
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: R01NS110915
                Award Recipient :
                Funded by: - USA ARL W911NF-18-20285
                Funded by: Czech Science Foundation (GACR) grant GA21-20374S
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: ERC-2015-AdG #694918
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000854, Human Frontier Science Program (HFSP);
                Award ID: RGP0043/2019
                Award ID: RGP0043/2019
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100005005, Ben-Gurion University of the Negev (Ben-Gurion University);
                Funded by: The Kreitman School of Advanced Graduate Studies
                Funded by: - The Israel Ministry of Science, Technology and Space.- Grant numnber MOST 3-14344 - The United States - Israel Binational Science Foundation. Grant number BSF 2019135
                Funded by: - Beckman Institute at Caltech
                Funded by: FundRef https://doi.org/10.13039/100007406, Fundación BBVA (BBVA Foundation);
                Funded by: Czech Ministry of Education, Youth and Sports national research infrastructure Czech-BioImaging projects LM2018129 and CZ.02.1.01/0.0/0.0/18_046/0016045
                Categories
                Analysis
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                © Springer Nature America, Inc. 2023

                Life sciences
                image processing,computational platforms and environments
                Life sciences
                image processing, computational platforms and environments

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