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Bioinformatics. 2014 Jun 1;30(11):1609-17. doi: 10.1093/bioinformatics/btu080. Epub 2014 Feb 12.

A benchmark for comparison of cell tracking algorithms.

Author information

1
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, First Faculty of Medicine, Charles University in Prague, 12801 Prague 2, Czech Republic and Biomedical Image Technologies, Universidad Politécnica de Madrid & 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
2
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, First Faculty of Medicine, Charles University in Prague, 12801 Prague 2, Czech Republic and Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER BBN, 28040 Madrid, Spain.

Abstract

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.

PMID:
24526711
PMCID:
PMC4029039
DOI:
10.1093/bioinformatics/btu080
[Indexed for MEDLINE]
Free PMC Article

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