The Cell Tracking Challenge: 10 years of objective benchmarking
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 18, 2023
- Source ID
- 10.1038/s41592-023-01879-y
Entities
People
- Ainhoa Urbiola
- Alexandre Cunha
- Andrew R. Cohen
- Arrate Muñoz Barrutia
- Assaf Arbelle
- Carlos Ortiz de Solórzano
- Cristina Ederra
- Elliot Meyerowitz
- Erik Meijering
- Estibaliz Gómez-de-Mariscal
- Fabian Isensee
- Fidel Alejandro Guerrero Peña
- Filip Lux
- Gani Rahmon
- Imad Eddine Toubal
- Jan P. Allebach
- Kannappan Palaniappan
- Katharina Löffler
- Klas E G Magnusson
- Klaus H. Maier-hein
- Ko Sugawara
- Layton Aho
- Martin Maška
- Michal Kozubek
- Noor M. Al-shakarji
- Pablo Delgado-Rodriguez
- Paul F. Jäger
- Petr Matula
- Ralf Mikut
- Rina Bao
- Tal Ben-haim
- Tammy Riklin Raviv
- Tereza Nečasová
- Tianqi Guo
- Tim Scherr
- Tsang Ing Ren
- Vladimír Ulman
- Yanming Zhu
- Yin Wang