We present a novel framework for high-throughput live cell lineage
analysis in time-lapse microscopy images. Our algorithm ties together
two fundamental aspects of live 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 algorithm, to achieve
posterior probabilities for cell segmentation and association. Highly
accurate results on five different cell-tracking datasets are achieved
and are presented in the following video:
. The proposed method’s
results where compared and surpassed current state of the art methods.
Joint work with N. Drayman, M. Bray, U. Alon, A. Carpenter, and T. Riklin-Raviv.