Journal
NATURE METHODS
Volume 14, Issue 12, Pages 1141-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nmeth.4473
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Funding
- Spanish Ministry of Economy MINECO [DPI2012-38090-C03-02, DPI2015-64221-C2-2, TEC2013-48552-C2-1-R, TEC2015-73064-EXP, TEC2016-78052-R]
- Netherlands Organization for Scientific Research (NWO) [612.001.018, 639.021.128]
- Dutch Technology Foundation (STW) [10443]
- Czech Science Foundation (GACR) [P302/12/G157]
- Czech Ministry of Education, Youth and Sports [LTC17016]
- Helmholtz Association
- DFG [MI 1315/4-1, SFB 1129, RTG 1653]
- Excellence Initiative of the German Federal Government [EXC 294]
- Excellence Initiative of the German State Government [EXC 294]
- Swiss Commission for Technology and Innovation, CTI project [16997]
- BMBF
- project ENGINE (NGFN+)
- project RNA-Code (e:Bio)
- project de.NBI
- HGS MathComp Graduate School [SFB 1129, RTG 1653]
- CellNetworks Excellence Cluster/EcTop
- Baxter Foundation
- US National Institutes of Health [AG020961]
- Swedish Research Council VR [2015-04026]
- BMBF, project de.NBI [031L0102]
- Swedish Research Council [2015-04026] Funding Source: Swedish Research Council
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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.
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