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.

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Recommended citation:

@article{mavska2023cell, title={The Cell Tracking Challenge: 10 years of objective benchmarking}, author={Ma{\v{s}}ka, Martin and Ulman, Vladim{'\i}r and Delgado-Rodriguez, Pablo and G{'o}mez-de-Mariscal, Estibaliz and Ne{\v{c}}asov{'a}, Tereza and Guerrero Pe{~n}a, Fidel A and Ren, Tsang Ing and Meyerowitz, Elliot M and Scherr, Tim and L{"o}ffler, Katharina and others}, journal={Nature Methods}, pages={1–11}, year={2023}, publisher={Nature Publishing Group US New York} }