Delgado-Rodríguez, P. (Pablo)

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    The cell tracking challenge: 10 years of objective benchmarking.
    (2023) Maska, M. (Martín); Ulman, V. (Vladimir); Delgado-Rodríguez, P. (Pablo); Gómez-de-Mariscal, E. (Estibaliz); Necasova, T. (Tereza); Guerrero-Peña, F.A. (Fidel A.); Ren, T.I. (Tsang Ing); Meyerowitz, E.M. (Elliot M.); Scherr, T. (Tim); Löffler, K. (Katharina); Mikut, R. (Ralf); Guo, T. (Tianqi); Wang, Y. (Yin); Allebach, J.P. (Jan P.); Bao, R. (Rina); Al-Shakarji, N.M. (Noor M.); Rahmon, G. (Gani); Toubal, I.E. (Imad Eddine); Palaniappan, K. (Kannappan); Lux, P. (Pilip); Matula, P. (Petr); Sugawara, G. (Go); Magnusson, K.E.G. (Klas E.G.); Aho, L. (Layton); Cohen, A.R. (Andrew R.); Arbelle, A. (Assaf); Ben-Haim, T. (Tal); Raviv, T.R. (Tammy Riklin); Issensee, F. (Fabian); Jäger, P.F.(Paul F.); Maier-Hein, K.H. (Klaus H.); Zhu, Y. (Yanming); Ederra, C. (Cristina); Urbiola, A. (Ainhoa); Meijering, E. (Erik); Cunha, A. (Alexandre); Muñoz-Barrutia, A. (Arrate); Kozubek, M. (Michal); Ortiz-de-Solorzano, C. (Carlos)
    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.