Ulman Vladimír, Maška Martin, Magnusson Klas E G, Ronneberger Olaf, Haubold Carsten, Harder Nathalie, Matula Pavel, Matula Petr, Svoboda David, Radojevic Miroslav, Smal Ihor, Rohr Karl, Jaldén Joakim, Blau Helen M, Dzyubachyk Oleh, Lelieveldt Boudewijn, Xiao Pengdong, Li Yuexiang, Cho Siu-Yeung, Dufour Alexandre 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 Ömer, Malmström Lars, Jug Florian, Tomancak Pavel, Meijering Erik, Muñoz-Barrutia Arrate, Kozubek Michal, Ortiz-de-Solorzano Carlos
Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic.
ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden.
Nat Methods. 2017 Dec;14(12):1141-1152. doi: 10.1038/nmeth.4473. Epub 2017 Oct 30.
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.
我们呈现了一份关于细胞追踪挑战赛三个版本结果的综合报告，这是一项正在进行的倡议活动，旨在推动细胞分割和追踪算法的发展及客观评估。该挑战赛有21种参与算法以及一个由来自各种显微镜模式的13个数据集组成的数据存储库，展示了该领域当今的前沿方法。我们使用对所有参与方法进行排名的分割和追踪性能指标来分析挑战赛结果。我们还从生物学指标和实际可用性方面分析了所有算法的性能。尽管有些方法在所有技术方面得分都很高，但没有一个能得到完全正确的解决方案。我们发现，在挑战赛所包含的分割和追踪场景下，要么使用学习策略考虑先验信息，要么在全局时空视频背景下分析细胞的方法，比其他方法表现更好。
