A benchmark for comparison of cell tracking algorithms
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, P.H.S.
Grant support
R01 AG009521
NIA NIH HHS - United States
R01 AG020961
NIA NIH HHS - United States
R01 HL096113
NHLBI NIH HHS - United States
R21 AR062359
NIAMS NIH HHS - United States
PubMed
24526711
PubMed Central
PMC4029039
DOI
10.1093/bioinformatics/btu080
PII: btu080
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Benchmarking MeSH
- Cell Tracking methods MeSH
- Microscopy, Fluorescence MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, P.H.S. MeSH
MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.
See more in PubMed
Al-Kofahi O, et al. Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle. 2006;5:327–335. PubMed
Bise R, et al. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011. Reliable cell tracking by global data association; pp. 1004–1010.
Carpenter AE, et al. A call for bioimaging software usability. Nat. Methods. 2012;7:666–670. PubMed PMC
Chenouard N, et al. Multiple-hypothesis tracking for cluttered biological image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 2013;35:2736–3750. PubMed
Dima AA, et al. Comparison of segmentation algorithms for fluorescence microscopy images of cells. Cytometry A. 2011;79:545–559. PubMed
Dufour A, et al. Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans. Image Process. 2005;14:1396–1410. PubMed
Dufour A, et al. 3-D active meshes: fast discrete deformable models for cell tracking in 3-D time-lapse microscopy. IEEE Trans. Image Process. 2011;20:1925–1937. PubMed
Dzyubachyk O, et al. Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans. Med. Imaging. 2010;29:852–867. PubMed
Fernandez-Gonzalez R, et al. Quantitative in vivo microscopy: the return from the ‘omics’. Curr. Opin. Biotechnol. 2006;17:501–510. PubMed
Foggia P, et al. Benchmarking HEp′2 cells classification methods. IEEE Trans. Med. Imaging. 2013;32:1878–1889. PubMed
Friedl P, Alexander D. Cancer invasion and the microenvironment: plasticity and reciprocity. Cell. 2011;147:992–1009. PubMed
Friedl P, Gilmour D. Collective cell migration in morphogenesis, regeneration and cancer. Nat. Rev. Mol. Cell Biol. 2009;10:445–457. PubMed
Held C, et al. Comparison of parameter-adapted segmentation methods for fluorescence micrographs. Cytometry A. 2011;79:933–945. PubMed
Indhumathi C, et al. An automatic segmentation algorithm for 3D cell cluster splitting using volumetric confocal images. J. Microsc. 2011;243:60–76. PubMed
Kan A, et al. Automated and semi-automated tracking: addressing portability challenges. J. Microsc. 2011;244:194–213. PubMed
Legant WR, et al. Measurement of mechanical tractions exerted by cells in three-dimensional matrices. Nat. Methods. 2010;7:969–971. PubMed PMC
Li K, et al. Cell population tracking and lineage construction with spatiotemporal context. Med. Image Anal. 2008;12:546–566. PubMed PMC
Li F, et al. Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis. IEEE Trans. Med. Imaging. 2010;29:96–105. PubMed PMC
Lin G, et al. Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei. Cytometry A. 2005;63:20–33. PubMed
Long F, et al. Proceedings of the 4th IEEE International Symposium on Biomedical Imaging. 2007. Automatic segmentation of nuclei in 3D microscopy images of C. Elegans; pp. 536–539.
Magnusson KEG, Jaldén J. Proceedings of the 9th IEEE International Symposium on Biomedical Imaging. 2012. A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages; pp. 382–385.
Maška M, et al. Segmentation and shape tracking of whole fluorescent cells based on the Chan-Vese model. IEEE Trans. Med. Imaging. 2013;32:995–1006. PubMed
Meijering E, et al. Tracking in cell and developmental biology. Semin. Cell Dev. Biol. 2009;20:894–902. PubMed
Ortiz-de-Solorzano C, et al. Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J. Microsc. 1999;193:212–226. PubMed
Padfield D, et al. Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med. Image Anal. 2011;15:650–668. PubMed
Rapoport DH, et al. A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters. PLoS One. 2011;11:e27315. PubMed PMC
Rohr K, et al. Tracking and quantitative analysis of dynamic movements of cells and particles. Cold Spring Harb. Protoc. 2010;6 pdb.top80. PubMed
Svoboda D, et al. Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry. Cytometry A. 2009;75:494–509. PubMed
Svoboda D, Ulman V. International Conference on Image Analysis and Recognition. 2012. Generation of synthetic image datasets for time-lapse fluorescence microscopy; pp. 473–482.
Zimmer C, et al. Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Trans. Med. Imaging. 2002;21:1212–1221. PubMed
Zimmer C, et al. On the digital trail of mobile cells. IEEE Signal Process. Mag. 2006;23:54–62.
The Cell Tracking Challenge: 10 years of objective benchmarking
CytoPacq: a web-interface for simulating multi-dimensional cell imaging
Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison
An objective comparison of cell-tracking algorithms
Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs