Cell segmentation in time-lapse fluorescence microscopy with temporally varying sub-cellular fusion protein patterns.
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 abundance 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 essential. 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(N(4)) to O(N(2)), for an image with N(2) pixels, making it practical and scalable for high throughput microscopy imaging studies.
Bunyak
Filiz
F
Department of Computer Science, University of Missouri-Columbia, Columbia MO 65211-2060, USA.
Palaniappan
Kannappan
K
Chagin
Vadim
V
Cardoso
M
M
D000465:Algorithms; D002453:Cell Cycle; D002467:Cell Nucleus / Q000378:metabolism; D049452:Green Fluorescent Proteins / Q000378:metabolism; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data; D008856:Microscopy, Fluorescence / Q000379:methods* / Q000706:statistics & numerical data; D018809:Proliferating Cell Nuclear Antigen / Q000378:metabolism; D011993:Recombinant Fusion Proteins / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism*; D013997:Time Factors
D018809:Proliferating Cell Nuclear Antigen; D011993:Recombinant Fusion Proteins; D049452:Green Fluorescent Proteins