A novel cell orientation congruence descriptor for superpixel based epithelium segmentation in endometrial histology images

Abstract

Recurrent miscarriage can be caused by an abnormally high number of Uterine Natural Killer (UNK) cells in human female uterus lining. Recently a diagnosis protocol has been developed based on the ratio of UNK cells to stromal cells in endometrial biopsy slides immunohistochemically stained with Haematoxylin for all cells and CD56 as a marker for the UNK cells. The counting of UNK cells and stromal cells is an essential process in the protocol. However, the cell counts must not include epithelial cells from glandular structures and UNK cells from epithelium. In this paper, we propose a novel superpixel based epithelium segmentation algorithm based on the observation that neighbouring epithelial cells packed at the boundary of glandular structures or background tend to have similar local orientations. Our main contribution is a novel cell orientation congruence descriptor in a machine learning framework to differentiate between epithelial and non-epithelial cells.