Multi‐resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images

HighlightsA novel descriptor for segmentation of luminal and glandular epithelium.The descriptor exploits the relatively similar orientation of epithelial cells.Multi‐resolution extension of the proposed descriptor to improve the results.Detailed experimentation on a large dataset and on choice of neighbourhood. Graphical abstract Figure. No Caption available. Abstract It has been recently shown that recurrent miscarriage can be caused by abnormally high ratio of number of uterine natural killer (UNK) cells to the number of stromal cells in human female uterus lining. Due to high workload, the counting of UNK and stromal cells needs to be automated using computer algorithms. However, stromal cells are very similar in appearance to epithelial cells which must be excluded in the counting process. To exclude the epithelial cells from the counting process it is necessary to identify epithelial regions. There are two types of epithelial layers that can be encountered in the endometrium: luminal epithelium and glandular epithelium. To the best of our knowledge, there is no existing method that addresses the segmentation of both types of epithelium simultaneously in endometrial histology images. In this paper, we propose a multi‐resolution Cell Orientation Congruence (COCo) descriptor which exploits the fact that neighbouring epithelial cells exhibit similarity in terms of their orientations. Our experimental results show that the proposed descriptors yield accurate results in simultaneously segmenting both luminal and glandular epithelium.

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