Maška, Martin;Ulman, Vladimír;Delgado-Rodriguez, Pablo;Gómez-de-Mariscal, Estibaliz;Nečasová, Tereza;Guerrero Peña, Fidel A.;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;Riklin Raviv, Tammy;Isensee, Fabian;Jäger, Paul F.;Maier-Hein, Klaus H.;Zhu, Yanming;Ederra, Cristina;Urbiola, Ainhoa;Meijering, Erik;Cunha, Alexandre;Muñoz-Barrutia, Arrate;Kozubek, Michal;Ortiz-de-Solórzano, Carlos;

The cell tracking challenge: 10 years of objective benchmarking.

Nature Methods, 2023.

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.

Citations: 91
© 2023 Imad Toubal.