Automated tracking of cells from phase contrast images by multiple hypothesis Kalman filters

Abstract

Cell migration is a fundamental process for the development and maintenance of all multicellular organisms. Accurate cell tracking may lead to better interpretations of long-term cell behaviours. This paper describes an automated system to track multiple cells from experimental phase contrast images, which includes image registration, lumen segmentation, cell candidate detection, and multiple hypothesis Kalman filtering. We incorporate biological knowledge to associate the new observations to existing tracks. We apply our methodology to the problem of tracking endothelial cells in 3D angiogenic vessels. Numerical results indicate that our method associates cells more accurately compared to standard methods for cll association and tracking.