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Nat Methods. 2014 Mar;11(3):281-9. doi: 10.1038/nmeth.2808. Epub 2014 Jan 19.

Objective comparison of particle tracking methods.

Author information

  • 11] Institut Pasteur, Unité d'Analyse d'Images Quantitative, Centre National de la Recherche Scientifique Unité de Recherche Associée 2582, Paris, France. [2] Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. [3] New York University Neuroscience Institute, New York University Medical Center, New York, New York, USA. [4].
  • 21] Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. [2] Department of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands. [3].
  • 31] Institut Pasteur, Unité d'Analyse d'Images Quantitative, Centre National de la Recherche Scientifique Unité de Recherche Associée 2582, Paris, France. [2].
  • 41] Center for Applied Medical Research, University of Navarra, Pamplona, Spain. [2] Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic. [3].
  • 5MOSAIC Group, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
  • 6Compunetix Inc., Monroeville, Pennsylvania, USA.
  • 7Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA.
  • 81] Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany. [2] Division of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany.
  • 91] Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. [2] Belozersky Institute of Physico-Chemical Biology, Moscow State University, Moscow, Russia.
  • 10Department of Electrical Engineering, Yale University, New Haven, Connecticut, USA.
  • 11Department of Cell Biology, Yale University, New Haven, Connecticut, USA.
  • 12Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
  • 13Department of Signal Processing, ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden.
  • 14Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.
  • 15Cell and Tissue Imaging Facility, Institut Curie, Paris, France.
  • 16Inria Rennes, Bretagne Atlantique, Rennes, France.
  • 17Plateforme d'Imagerie Dynamique, Imagopole, Institut Pasteur, Paris, France.
  • 18Molecular Biotechnology Group, Institute of Biology, Leiden University, Leiden, The Netherlands.
  • 19Department of Biomedical Engineering, Chung Yuan Christian University, Chung Li City, Taiwan, China.
  • 20Center for Applied Medical Research, University of Navarra, Pamplona, Spain.

Abstract

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

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PMID:
24441936
[PubMed - indexed for MEDLINE]
PMCID:
PMC4131736
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