A Framework for Automated Cell Tracking in Phase Contrast Microscopic Videos based on Normal Velocities
This addresses the problem of labor-intensive manual cell tracking in biomedical research, offering an automated solution for specific cell types, though it appears incremental as it builds on existing optical flow and segmentation methods.
The paper tackles automated cell tracking in phase contrast microscopic videos by developing a framework based on normal velocities from optical flow and variational segmentation, with an active contour correction step for shape features. Results show robust tracking validated against manual tracking for renal epithelial, melanoma, and neutrophil cells on different matrices.
This paper introduces a novel framework for the automated tracking of cells, with a particular focus on the challenging situation of phase contrast microscopic videos. Our framework is based on a topology preserving variational segmentation approach applied to normal velocity components obtained from optical flow computations, which appears to yield robust tracking and automated extraction of cell trajectories. In order to obtain improved trackings of local shape features we discuss an additional correction step based on active contours and the image Laplacian which we optimize for an example class of transformed renal epithelial (MDCK-F) cells. We also test the framework for human melanoma cells and murine neutrophil granulocytes that were seeded on different types of extracellular matrices. The results are validated with manual tracking results.