The Cell Tracking Challenge: 10 years of objective benchmarking

Logo poskytovatele
Logo poskytovatele

Varování

Publikace nespadá pod Ústav výpočetní techniky, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
Autoři

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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info