We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

ACCESS Linnaeus Centre KTH Royal Institute of Technology Stockholm Sweden

Baxter Laboratory for Stem Cell Biology Department of Microbiology and Immunology and Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine Stanford California USA

Bioengineering and Aerospace Engineering Department Universidad Carlos 3 de Madrid Getafe Spain

Bioengineering Department TECNUN School of Engineering University of Navarra San Sebastián Spain

BioImage Analysis Unit Institut Pasteur Paris France

Biomedical Computer Vision Group Department of Bioinformatics and Functional Genomics BIOQUANT IPMB University of Heidelberg and DKFZ Heidelberg Germany

Biomedical Imaging Group Rotterdam Departments of Medical Informatics and Radiology Erasmus University Medical Center Rotterdam Rotterdam the Netherlands

Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic

CIBERONC IDISNA and Program of Solid Tumors and Biomarkers Center for Applied Medical Research University of Navarra Pamplona Spain

Computer Science Department and BIOSS Centre for Biological Signaling Studies University of Freiburg Frieburg Germany

Department of Engineering University of Nottingham Nottingham UK

Division of Image Processing Department of Radiology Leiden University Medical Center Leiden the Netherlands

Facultade de Engenharia Universidade do Porto Porto Portugal

Faculty of Engineering University of Nottingham Ningbo China

Group for Automated Image and Data Analysis Institute for Applied Computer Science Karlsruhe Institute of Technology Eggenstein Leopoldshafen Germany

Heidelberg Collaboratory for Image Processing IWR University of Heidelberg Heidelberg Germany

i3S Instituto de Investigação e Inovação em Saúde Universidade do Porto Porto Portugal

Institute of Molecular and Cell Biology A*Star Singapore

Instituto de Investigación Sanitaria Gregorio Marañon Madrid Spain

Intelligent Systems Department Delft University of Technology Delft the Netherlands

Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany

Research Centre in Biomedical Engineering School of Mathematics Computer Science and Engineering City University of London London UK

S3IT University of Zurich Zurich Switzerland

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