Li, Guannan, Raza, Shan-e-Ahmed and Rajpoot, Nasir M. (2017) Multi-resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images. Medical Image Analysis, 37 . pp. 91-100. doi:10.1016/j.media.2017.01.006

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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.

Item Type:	Journal Article
Subjects:	Q Science > QM Human anatomy R Medicine > RG Gynecology and obstetrics
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Endothelium -- Histology, Miscarriage -- Prevention, Uterus -- Physiology, Generative organs, Female
Journal or Publication Title:	Medical Image Analysis
Publisher:	Elsevier Science BV
ISSN:	1361-8415
Official Date:	April 2017
Dates:	Date Event April 2017 Published 21 January 2017 Available 19 January 2017 Accepted 2 September 2016 Submitted
Volume:	37
Page Range:	pp. 91-100
DOI:	10.1016/j.media.2017.01.006
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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