Publicaciones científicas
- [SISTEMAS MICROFISIOLÓGICOS Y BIOLOGÍA CUANTITATIVA]
- [INGENIERÍA BIOMÉDICA]
- [SISTEMAS MICROFISIOLÓGICOS Y BIOLOGÍA CUANTITATIVA]
The Cell Tracking Challenge: 10 years of objective benchmarking
Martin Maška 1, Vladimír Ulman 1 2, Pablo Delgado-Rodriguez 3 4, Estibaliz Gómez-de-Mariscal 3 4 5, Tereza Nečasová 1, Fidel A Guerrero Peña 6 7, Tsang Ing Ren 6, Elliot M Meyerowitz 8, Tim Scherr 9, Katharina Löffler 9, Ralf Mikut 9, Tianqi Guo 10, Yin Wang 10, Jan P Allebach 10, Rina Bao 11 12, Noor M Al-Shakarji 12, Gani Rahmon 12, Imad Eddine Toubal 12, Kannappan Palaniappan 12, Filip Lux 1, Petr Matula 1, Ko Sugawara 13 14, Klas E G Magnusson 15, Layton Aho 16, Andrew R Cohen 16, Assaf Arbelle 17, Tal Ben-Haim 17, Tammy Riklin Raviv 17, Fabian Isensee 18 19, Paul F Jäger 19 20, Klaus H Maier-Hein 18 21, Yanming Zhu 22 23, Cristina Ederra 24, Ainhoa Urbiola 24, Erik Meijering 22, Alexandre Cunha 7, Arrate Muñoz-Barrutia 3 4, Michal Kozubek # 25, Carlos Ortiz-de-Solórzano # 26
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.
CITA DEL ARTÍCULO Nat Methods. 2023 May 18. doi: 10.1038/s41592-023-01879-y
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