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The Cell Tracking Challenge: 10 years of objective benchmarking

2023; Nature Portfolio; Volume: 20; Issue: 7 Linguagem: Inglês

10.1038/s41592-023-01879-y

1548-7105

Martin Maška, Vladimír Ulman, Pablo Delgado-Rodriguez, Estibaliz Gómez‐de‐Mariscal, Tereza Nečasová, Fidel A. Guerrero Peña, Tsang Ing Ren, Elliot M. Meyerowitz, Tim Scherr, Katharina Löffler, Ralf Mikut, Tianqi Guo, Yin Wang, Jan P. Allebach, Rina Bao, Noor Al-Shakarji, Gani Rahmon, Imad Eddine Toubal, Kannappan Palaniappan, Filip Lux, Petr Matula, Ko Sugawara, Klas E. G. Magnusson, Layton Aho, Andrew R. Cohen, Assaf Arbelle, Tal Ben-Haim, Tammy Riklin Raviv, Fabian Isensee, Paul F. Jäger, Klaus Maier‐Hein, Yanming Zhu, Cristina Ederra, Ainhoa Urbiola, Erik Meijering, Alexandre Cunha, Arrate Muñoz‐Barrutia, Michal Kozubek, Carlos Ortiz‐de‐Solórzano,

Advanced Fluorescence Microscopy Techniques

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.