2012/03/27 by Michael Moeller, Moeller, Michael, Martin Burger +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Vision and Imaging #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Medical Image Segmentation Techniques #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1203.5914
openalex publication_date 2012/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.