The Cell Tracking Challenge: 10 years of objective benchmarking

Autor: 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 M. 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 H. Maier-Hein, Yanming Zhu, Cristina Ederra, Ainhoa Urbiola, Erik Meijering, Alexandre Cunha, Arrate Muñoz-Barrutia, Michal Kozubek, Carlos Ortiz-de-Solórzano
Rok vydání: 2023
Předmět:
Zdroj: Nature Methods.
ISSN: 1548-7105
1548-7091
DOI: 10.1038/s41592-023-01879-y
Popis: 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.
Databáze: OpenAIRE
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