Graph based method for cell segmentation and detection in live-cell fluorescence microscope imaging

Abstract

Live-cell fluorescence image segmentation is an essential step in many studies, including in drug research and other contexts where keeping cells alive is crucial. Several segmentation algorithms and programs have been previously proposed; however, they do not work sufficiently well on top-down pictures with overlapping cells. Our proposed algorithm, called GRABaCELL, utilizes Graph Cut, Watershed segmentation and Hough Circular Transform to improve automatic segmentation and counting living cells. We also introduce a modified accuracy metric to determine the quality of segmentation in terms of the number of cells detected in the image. The GRABaCELL method results are vastly better in visual assessment, by both Dice index and modified accuracy metric, than all other compared methods maintaining not only a high value of these indices but also a relatively small spread.