An objective comparison of cell-tracking algorithms

Vladimír Ulman, Martin Maška, Klas E. G. Magnusson, Olaf Ronneberger, Carsten Haubold, Nathalie Harder, Pavel Matula, Petr Matula, David Svoboda, Miroslav Radojevic, Ihor Smal, Karl Rohr, Joakim Jaldén, Helen M. Blau, Oleh Dzyubachyk, Boudewijn Lelieveldt

Research output: Contribution to journalArticleScientificpeer-review

74 Citations (Scopus)

Abstract

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

Original languageEnglish
Pages (from-to)1141-1152
Number of pages12
JournalNature Methods (online)
Volume14
Issue number12
DOIs
Publication statusPublished - 2017

Keywords

  • Cell migration
  • Image processing

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    Ulman, V., Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., & Lelieveldt, B. (2017). An objective comparison of cell-tracking algorithms. Nature Methods (online), 14(12), 1141-1152. https://doi.org/10.1038/nmeth.4473