An objective comparison of cell-tracking algorithms Articles uri icon

authors

  • ULMAN, VLADIMIR
  • MASKA, MARTIN
  • MAGNUSSON, KLAS E.G.
  • RONNEBERGER, OLAF
  • HAUBOLD, CARSTEN
  • HARDER, NATHALIE
  • MATULA, PAVEL
  • MATULA, PERT
  • SVOBODA, DAVID
  • RADOJEVIC, MIROSLAV
  • SMAL, IHOR
  • ROHR, KARL
  • JALDEN, JOAKIM
  • BLAU, HELEN M.
  • DZYUBACHYK, OLEH
  • LELIEVELDT, BOUDEWIJN
  • XIAO, PENGDONG
  • LI, YUEXIANG
  • CHO, SIUYEUNG
  • DUFOUR, ALEXANDER C.
  • OLIVO MARIN, JEAN CHRISTOPHE
  • REYES ALDASORO, CONSTANTINO C.
  • SOLIS LEMUS, JOSE A.
  • BENSCH, ROBERT
  • BROX, THOMAS
  • STEGMAIER, JOHANNES
  • MIKUT, RALF
  • WOLF, STEFFEN
  • HAMPRECHT, FRED A.
  • ESTEVES, TIAGO
  • QUELHAS, PEDRO
  • DEMIREL, OMER
  • MALMSTROM, LARS
  • JUG, FLORIAN
  • TOMANCAK, PAVEL
  • MEIJERING, ERIK
  • MUÑOZ BARRUTIA, MARIA ARRATE
  • Kozubek, Michal
  • ORTIZ DE SOLÓRZANO, CARLOS

publication date

  • October 2017

start page

  • 1141

end page

  • 1152

issue

  • 12

volume

  • 14

International Standard Serial Number (ISSN)

  • 1548-7091

Electronic International Standard Serial Number (EISSN)

  • 1548-7105

abstract

  • 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.

keywords

  • protein; algorithm; article; caenorhabditis elegans; cell cycle; cell motion; cell tracking; chromatin; chromatin condensation; drosophila melanogaster; fluorescence; image segmentation; microscopy; nonhuman; priority journal; procedures; benchmarking; cell line; computer assisted diagnosis; human; algorithms; humans; image interpretation; computer-assisted