#92: Cell segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns


F. Bunyak, K. Palaniappan, V. Chagin, and M. C. Cardoso

31st IEEE Engineering in Medicine and Biology Society Conf. (EMBC), pgs. 1--5, 2009

cell detection, cell segmentation, cell tracking, features, texture, biomedical

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Abstract

Fluorescently tagged proteins such as GFP-PCNA produce rich dynamically varying textural patterns of foci distributed in the nucleus. This enables the behavioral study of sub-cellular structures during different phases of the cell cycle. The varying punctuate patterns of fluorescence, drastic changes in SNR, shape and position during mitosis and abun- dance of touching cells, however, require more sophisticated algorithms for reliable automatic cell segmentation and lineage analysis. Since the cell nuclei are non-uniform in appearance, a distribution-based modeling of foreground classes is essen- tial. The recently proposed graph partitioning active contours (GPAC) algorithm supports region descriptors and flexible distance metrics. We extend GPAC for fluorescence-based cell segmentation using regional density functions and dramatically improve its efficiency for segmentation from O(N4) to O(N2), for an image with N2 pixels, making it practical and scalable for high throughput microscopy imaging studies.