The Cell Tracking Challenge: 10 years of objective benchmarking

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MAŠKA Martin ULMAN Vladimír DELGADO-RODRIGUEZ Pablo GÓMEZ-DE-MARISCAL Estibaliz NEČASOVÁ Tereza PENA Fidel A Guerrero REN Tsang Ing MEYEROWITZ Elliot M SCHERR Tim LÖFFLER Katharina MIKUT Ralf GUO Tianqi WANG Yin ALLEBACH Jan P BAO Rina AL-SHAKARJI Noor M RAHMON Gani TOUBAL Imad Eddine PALANIAPPAN Kannappan LUX Filip MATULA Petr SUGAWARA Ko MAGNUSSON Klas E G AHO Layton COHEN Andrew R ARBELLE Assaf BEN-HAIM Tal RAVIV Tammy Riklin ISENSEE Fabian JÄGER Paul F MAIER-HEIN Klaus H ZHU Yanming EDERRA Cristina URBIOLA Ainhoa MEIJERING Erik CUNHA Alexandre MUNOZ-BARRUTIA Arrate KOZUBEK Michal ORTIZ-DE-SOLÓRZANO Carlos

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj Nature Methods
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://doi.org/10.1038/s41592-023-01879-y
Doi http://dx.doi.org/10.1038/s41592-023-01879-y
Klíčová slova cell segmentation;cell tracking;benchmarking
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.
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