An objective comparison of cell-tracking algorithms View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-10-30

AUTHORS

Vladimír Ulman, Martin Maška, Klas E G Magnusson, Olaf Ronneberger, Carsten Haubold, Nathalie Harder, Pavel Matula, Petr Matula, David Svoboda, Miroslav Radojevic, Ihor Smal, Karl Rohr, Joakim Jaldén, Helen M Blau, Oleh Dzyubachyk, Boudewijn Lelieveldt, Pengdong Xiao, Yuexiang Li, Siu-Yeung Cho, Alexandre C Dufour, Jean-Christophe Olivo-Marin, Constantino C Reyes-Aldasoro, Jose A Solis-Lemus, Robert Bensch, Thomas Brox, Johannes Stegmaier, Ralf Mikut, Steffen Wolf, Fred A Hamprecht, Tiago Esteves, Pedro Quelhas, Ömer Demirel, Lars Malmström, Florian Jug, Pavel Tomancak, Erik Meijering, Arrate Muñoz-Barrutia, Michal Kozubek, Carlos Ortiz-de-Solorzano

ABSTRACT

This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers.

PAGES

1141-1152

Journal

TITLE

Nature Methods

ISSUE

12

VOLUME

14

Author Affiliations

  • Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
  • Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
  • ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden
  • DeepMind, London, UK
  • Heidelberg Collaboratory for Image Processing, IWR, University of Heidelberg, Heidelberg, Germany
  • Definiens AG, Munich, Germany
  • Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
  • Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, BIOQUANT, IPMB, University of Heidelberg and DKFZ, Heidelberg, Germany
  • Department of Microbiology and Immunology, Baxter Laboratory for Stem Cell Biology, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA
  • Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
  • Intelligent Systems Department, Delft University of Technology, Delft, The Netherlands
  • National Heart Research Institute Singapore (NHRIS), National Heart Centre Singapore (NHCS), Singapore
  • Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
  • Faculty of Engineering, University of Nottingham, Ningbo, China
  • BioImage Analysis Unit, Institut Pasteur, Paris, France
  • Research Centre in Biomedical Engineering, School of Mathematics, Computer Science and Engineering, City University of London, London, UK
  • Computer Science Department and BIOSS Centre for Biological Signaling Studies University of Freiburg, Frieburg, Germany
  • Group for Automated Image and Data Analysis, Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
  • Facultade de Engenharia, Universidade do Porto, Porto, Portugal
  • i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
  • S3IT, University of Zurich, Zurich, Switzerland
  • Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
  • Bioengineering Department, TECNUN School of Engineering, University of Navarra, San Sebastián, Spain
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nmeth.4473

    DOI

    http://dx.doi.org/10.1038/nmeth.4473

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092440092

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29083403


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