| Autor: |
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, Raviv, Tammy Riklin, 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 |
| Zdroj: |
Nature Methods; July 2023, Vol. 20 Issue: 7 p1010-1020, 11p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
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