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

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.

Automated summary

The GRABaCELL algorithm combines Graph Cut, Watershed segmentation, and Hough Circular Transform to improve automatic segmentation and counting of living cells. The introduced modified accuracy metric helps assess the quality of segmentation based on the number of cells detected in the image. The results of the GRABaCELL method outperform other compared methods in visual assessment, with both Dice index and modified accuracy metric showing significantly better performance.