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This paper introduces a novel framework for the automated tracking of cells,with a particular focus on the challenging situation of phase contrastmicroscopic videos. Our framework is based on a topology preserving variationalsegmentation approach applied to normal velocity components obtained fromoptical flow computations, which appears to yield robust tracking and automatedextraction of cell trajectories. In order to obtain improved trackings of localshape features we discuss an additional correction step based on activecontours and the image Laplacian which we optimize for an example class oftransformed renal epithelial (MDCK-F) cells. We also test the framework forhuman melanoma cells and murine neutrophil granulocytes that were seeded ondifferent types of extracellular matrices. The results are validated withmanual tracking results.

A Framework for Automated Cell Tracking in Phase Contrast Microscopic Videos based on Normal Velocities