Spatiotemporal Bayesian cell population tracking and analysis with lineage construction

Abstract

Tracking of cell populations in vitro in time lapse microscopy images enables automatic high throughput spatiotemporal measurements of a range of cell cycle mechanics and dynamics. Both in clinical and academic environments, large scale cellular data analysis using such methods stands to facilitate a paradigm shift in approaches to understanding cell biology. In this paper, we present a novel approach to cell population tracking and segmentation. We employ the CONDENSATION algorithm in tandem with Fast Levels Sets and Exclusion Zones for robust tracking and pixel-accurate segmentation. The algorithm feeds its output to a lineage filter. The complete approach is validated in terms of its ability to track and identify nuclei, and by its success in detecting abnormalities in the length of mitosis.