Abstract
We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.
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