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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
The Cell Tracking Challenge: 10 years of objective benchmarking
Nature methods, v 20(7), pp 1010-1020
18 May 2023
PMID: 37202537
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- The Cell Tracking Challenge: 10 years of objective benchmarking
- Creators
- Martin Maška - Masaryk UniversityVladimír Ulman - VSB - Technical University of OstravaPablo Delgado-Rodriguez - Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, SpainEstibaliz Gómez-de-Mariscal - Instituto Gulbenkian de CiênciaTereza Nečasová - Masaryk UniversityFidel A Guerrero Peña - Center for Advanced Methods in Biological Image Analysis, Beckman Institute, California Institute of Technology, Pasadena, CA, USATsang Ing Ren - Universidade Federal de PernambucoElliot M Meyerowitz - Howard Hughes Medical InstituteTim Scherr - Karlsruhe Institute of TechnologyKatharina Löffler - Karlsruhe Institute of TechnologyRalf Mikut - Karlsruhe Institute of TechnologyTianqi Guo - Purdue University West LafayetteYin Wang - Purdue University West LafayetteJan P Allebach - Purdue University West LafayetteRina Bao - University of MissouriNoor M Al-Shakarji - University of MissouriGani Rahmon - University of MissouriImad Eddine Toubal - University of MissouriKannappan Palaniappan - University of MissouriFilip Lux - Masaryk UniversityPetr Matula - Masaryk UniversityKo Sugawara - Centre National pour la Recherche Scientifique et Technique (CNRST)Klas E G Magnusson - RaySearch Laboratories (Sweden)Layton Aho - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAAndrew R Cohen - Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USAAssaf Arbelle - Ben-Gurion University of the NegevTal Ben-Haim - Ben-Gurion University of the NegevTammy Riklin Raviv - Ben-Gurion University of the NegevFabian Isensee - Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, GermanyPaul F Jäger - Interactive Machine Learning Group, German Cancer Research Center (DKFZ), Heidelberg, GermanyKlaus H Maier-Hein - University Hospital HeidelbergYanming Zhu - Griffith UniversityCristina Ederra - Universidad de NavarraAinhoa Urbiola - Universidad de NavarraErik Meijering - UNSW SydneyAlexandre Cunha - Center for Advanced Methods in Biological Image Analysis, Beckman Institute, California Institute of Technology, Pasadena, CA, USAArrate Muñoz-Barrutia - Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, SpainMichal Kozubek - Masaryk UniversityCarlos Ortiz-de-Solórzano - Universidad de Navarra
- Publication Details
- Nature methods, v 20(7), pp 1010-1020
- Publisher
- Springer Nature
- Number of pages
- 11
- Grant note
- RGP0043/2019 / Human Frontier Science Program (HFSP) R01NS110915 / U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) EMBO-2020-IG- 4734 / European Molecular Biology Organization (EMBO) ERC-2015-AdG #694918 / EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) CZ.02.1.01/0.0/0.0/16_013/0001791 / EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj) EMBO ALTF 174-2022 / European Molecular Biology Organization (EMBO)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000999144000001
- Scopus ID
- 2-s2.0-85159660888
- Other Identifier
- 991020542878904721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods