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A benchmark for comparison of cell tracking algorithms

. 2014 Jun 01 ; 30 (11) : 1609-17. [epub] 20140212

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

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.

Center for Biomedical Image Analysis Masaryk University 602 00 Brno Czech Republic Cancer Imaging Laboratory Oncology Division Center for Applied Medical Research University of Navarra 31008 Pamplona Spain Biomedical Imaging Group Rotterdam Erasmus University Medical Center 3015 GE Rotterdam The Netherlands Fusion Technology and Systems Department Compunetix Inc Monroeville PA 15146 USA Biomedical Computer Vision Group Department of Bioinformatics and Functional Genomics University of Heidelberg BIOQUANT IPMB and DKFZ 69120 Heidelberg Germany KTH Royal Institute of Technology ACCESS Linnaeus Center Department of Signal Processing 100 44 Stockholm Sweden Baxter Laboratory for Stem Cell Biology Department of Microbiology and Immunology Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine Stanford CA 94305 USA Division of Image Processing Leiden University Medical Center 2300 RC Leiden The Netherlands Institute of Cellular Biology and Pathology 1st Faculty of Medicine Charles University Prague 12801 Prague 2 Czech Republic and Biomedical Image Technologies Universidad Politécnica de Madrid and CIBER BBN 28040 Madrid Spain

Center for Biomedical Image Analysis Masaryk University 602 00 Brno Czech Republic Cancer Imaging Laboratory Oncology Division Center for Applied Medical Research University of Navarra 31008 Pamplona Spain Biomedical Imaging Group Rotterdam Erasmus University Medical Center 3015 GE Rotterdam The Netherlands Fusion Technology and Systems Department Compunetix Inc Monroeville PA 15146 USA Biomedical Computer Vision Group Department of Bioinformatics and Functional Genomics University of Heidelberg BIOQUANT IPMB and DKFZ 69120 Heidelberg Germany KTH Royal Institute of Technology ACCESS Linnaeus Center Department of Signal Processing 100 44 Stockholm Sweden Baxter Laboratory for Stem Cell Biology Department of Microbiology and Immunology Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine Stanford CA 94305 USA Division of Image Processing Leiden University Medical Center 2300 RC Leiden The Netherlands Institute of Cellular Biology and Pathology 1st Faculty of Medicine Charles University Prague 12801 Prague 2 Czech Republic and Biomedical Image Technologies Universidad Politécnica de Madrid and CIBER BBN 28040 Madrid SpainCenter for Biomedical Image Analysis Masaryk University 602 00 Brno Czech Republic Cancer Imaging Laboratory Oncology Division Center for Applied Medical Research University of Navarra 31008 Pamplona Spain Biomedical Imaging Group Rotterdam Erasmus University Medical Center 3015 GE Rotterdam The Netherlands Fusion Technology and Systems Department Compunetix Inc Monroeville PA 15146 USA Biomedical Computer Vision Group Department of Bioinformatics and Functional Genomics University of Heidelberg BIOQUANT IPMB and DKFZ 69120 Heidelberg Germany KTH Royal Institute of Technology ACCESS Linnaeus Center Department of Signal Processing 100 44 Stockholm Sweden Baxter Laboratory for Stem Cell Biology Department of Microbiology and

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.

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