Tracking of cell population from time lapse and end point confocal microscopy images with multiple hypothesis Kalman smoothing filters

Abstract

This paper describes an automated visual tracking system combining time-lapse and end-point confocal microscopy to aid the interpretations of cell behaviors and interactions, with the focus on understanding the sprouting mechanism during angiogenesis. These multiple cells exhibit stochastic motion and are subjected to photo-bleaching and the images acquired are of low signal to noise ratio. Hence, following time-lapse imaging, high resolution end-point images are acquired. Our approach applies a probabilistic motion filter (a backward Kalman filtering followed by track smoothing) which incorporates end-point and all available time-lapse information in a mathematically consistent manner to obtain trajectory and phenotype information of multiple individual cells simultaneously. An extension of this algorithm, track smoothing with a Multiple Hypothesis Testing (MHT) data association, is proposed to improve association of multiple close contact and proliferating cells across images acquired from different time points to existing track trajectories. Our methodology was applied to tracking endothelial cell sprouting in three-dimensional micro-fluidic devices.