אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

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: https://www.youtube.com/watch?v=ORx82dCKWlA. 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.