title: Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs.
abstract: Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.
authors.affiliation: Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.|Department of Molecular Cytology and Cytometry, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic.
authors.forename: Pavel
authors.initials: P
authors.lastname: Matula
authors.affiliation: Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.
authors.forename: Martin
authors.initials: M
authors.lastname: Maška
authors.affiliation: Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.
authors.forename: Dmitry V
authors.initials: DV
authors.lastname: Sorokin
authors.affiliation: Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.
authors.forename: Petr
authors.initials: P
authors.lastname: Matula
authors.affiliation: Cancer Imaging Laboratory, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.
authors.forename: Carlos
authors.initials: C
authors.lastname: Ortiz-de-Solórzano
authors.affiliation: Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic.
authors.forename: Michal
authors.initials: M
authors.lastname: Kozubek
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D058948:Cell Tracking / Q000379:methods*; D006801:Humans; D008856:Microscopy, Fluorescence; D059008:Time-Lapse Imaging / Q000379:methods