The Cell Tracking Challenge: 10 years of objective benchmarking Articles uri icon

authors

  • Maška, Martin
  • Ulman, Vladimir
  • Delgado Rodriguez, Pablo
  • GOMEZ DE MARISCAL, ESTIBALIZ
  • Guerrero Peña, Fidel A.
  • Ren, Tsang Ing
  • Meyerowitz, Elliot M.
  • Scherr, Tim
  • Loffler, 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, MARIA ARRATE
  • Kozubek, Michal
  • Ortiz de Solorzano, Carlos

publication date

  • July 2023

issue

  • 7

volume

  • 20

International Standard Serial Number (ISSN)

  • 1548-7091

Electronic International Standard Serial Number (EISSN)

  • 1548-7105

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.

subjects

  • Biology and Biomedicine