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The Cell Tracking Challenge: 10 years of objective benchmarking
M. Maška, V. Ulman, P. Delgado-Rodriguez, E. Gómez-de-Mariscal, T. Nečasová, FA. Guerrero Peña, TI. Ren, EM. Meyerowitz, T. Scherr, K. Löffler, R. Mikut, T. Guo, Y. Wang, JP. Allebach, R. Bao, NM. Al-Shakarji, G. Rahmon, IE. Toubal, K....
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural, práce podpořená grantem
Grantová podpora
R01 NS110915
NINDS NIH HHS - United States
Howard Hughes Medical Institute - United States
NLK
ProQuest Central
od 2004-10-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2004-10-01 do Před 1 rokem
- MeSH
- algoritmy MeSH
- benchmarking * MeSH
- buněčný tracking * metody MeSH
- strojové učení MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
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.
Bioengineering Department Universidad Carlos 3 de Madrid Madrid Spain
Boston Children's Hospital and Harvard Medical School Boston MA USA
Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic
Centre National de la Recherche Scientifique Paris France
Centro de Informatica Universidade Federal de Pernambuco Recife Brazil
Department of Electrical and Computer Engineering Drexel University Philadelphia PA USA
Division of Medical Image Computing German Cancer Research Center Heidelberg Germany
Griffith University Nathan Queensland Australia
Helmholtz Imaging German Cancer Research Center Heidelberg Germany
Institut de Génomique Fonctionnelle de Lyon École Normale Supérieure de Lyon Lyon France
Instituto de Investigación Sanitaria Gregorio Marañón Madrid Spain
Interactive Machine Learning Group German Cancer Research Center Heidelberg Germany
Optical Cell Biology Instituto Gulbenkian de Ciência Oeiras Portugal
Raysearch Laboratories AB Stockholm Sweden
School of Electrical and Computer Engineering Ben Gurion University of the Negev Beersheba Israel
Citace poskytuje Crossref.org
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