#59: Robust tracking of migrating cells using four-color level set segmentation

S. K. Nath, F. Bunyak, and K. Palaniappan

Lecture Notes in Computer Science (ACIVS), Volume 4179, pgs. 920--932, 2006

cell detection, cell segmentation, active contours, cell tracking, biomedical

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Understanding behavior of migrating cells is becoming an emerging research area with many important applications. Segmentation and tracking con- stitute vital steps of this research. In this paper, we present an automated cell segmentation and tracking system designed to study migration of cells imaged with a phase contrast microscope. For segmentation the system uses active con- tour level set methods with a novel extension that efficiently prevents false-merge problem. Tracking is done by resolving frame to frame correspondences between multiple cells using a multi-distance, multi-hypothesis algorithm. Cells that move into the field-of-view, arise from cell division or disappear due to apoptosis are reliably segmented and tracked by the system. Robust tracking of cells, imaged with a phase contrast microscope is a challenging problem due to difficulties in segmenting dense clusters of cells. As cells being imaged have vague borders, close neighboring cells may appear to merge. These false-merges lead to incor- rect trajectories being generated during the tracking process. Current level-set based approaches to solve the false-merge problem require a unique level set per object (the N-level set paradigm). The proposed approach uses evidence from previous frames and graph coloring principles and solves the same problem with only four level sets for any arbitrary number of similar objects, like cells.