Symmetry-based presentation for stem-cell image segmentation

Cancer stem cells have been isolated from many tumors, including breast, brain, colon, head and neck, lung, pancreas, and prostate tumors. Advances in stem cell biology and animal models help better characterization of cancer stem cells, including the cells of origin, molecular and cellular properties, functions in cancer initiation and development, treatment response, and drug resistance. An important and challenging task in image analysis of stem cells is the image segmentation. A difficulty is to segment aggregated cells that are deformed and occluded. Watershed transform and multiscale morphological operation are the common methods for this purpose, as they are robust against arbitrary shaping and the occlusion of cells. Notwithstanding their high robustness, the two methods are still limited in their applications in the cases with cells suffering perturbations and deformation during cell growth. In this paper, we propose a novel symmetry axis transformation for stem-cell image segmentation. Our algorithm was validated by its comparison with both watershed transform and multiscale morphological operation. Improved segmentation performance in terms of precision (up to 2.2% comparing to watershed; and up to 0.6% comparing to multiscale morphological operation) was achieved using 5197 cell images in which 291 cells are three mutually touching.