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.
Highlights
The cornerstone of many modern live-cell imaging experiments is the ability to automatically track and analyze the motility of cells in time-lapse microscopy images [1, 2]
We study how sensitive the Acyclic oriented graphs matching (AOGM) measure is to the choice of weights, and how its behavior coincides with human expert appraisal
The AOGM measure, an accuracy measure for objective and systematic comparison of cell tracking algorithms that are capable of providing segmentation of individual cells rather than simplifying them as single-point objects, has been defined and analyzed
Summary
The cornerstone of many modern live-cell imaging experiments is the ability to automatically track and analyze the motility of cells in time-lapse microscopy images [1, 2]. The second computes the ratio of correct temporal relationships within reconstructed tracks to the total number of temporal relationships within ground-truth tracks [16, 17] Both approaches quantify, at different scales, how well the cell tracking algorithms are able to reconstruct a particular ground-truth reference. At different scales, how well the cell tracking algorithms are able to reconstruct a particular ground-truth reference They neither penalize detecting spurious tracks nor account for division events, which are often evaluated separately [4, 16]. It cannot be applied to cell tracking applications because the tracked objects can divide over time or disappear after undergoing cell death Another evaluation framework [19], established for comparing the performance of particle tracking methods, does not consider division events, ruling out its ability to evaluate correct cell lineage reconstruction. A slight modification in the definition of edge-related graph operations, which has no practical impact on the ranking compilation, allows us to formulate the necessary condition for the choice of weights, which guarantees the measure value to be the weighted sum of the lowest number of graph operations and the minimum weighted cost of transforming a computed graph into a given ground-truth reference
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