Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach

Song, Tzu-Hsi and Landini, Gabriel and Fouad, Shereen and Mehanna, Hisham (2019) Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach. In: IEEE International Symposium on Biomedical Imaging (ISBI 2019) . Institute of Electrical and Electronics Engineers (IEEE)., 8th-11th April 2019, Venice, Italy.

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Abstract

The assessment of pathological samples by molecular techniques, such as in situ hybridization (ISH) and immunohistochemistry (IHC), has revolutionised modern Histopathology. Most often it is important to detect ISH/IHC reaction products in certain cells or tissue types. For instance, detection of human papilloma virus (HPV) in oropharyngeal cancer samples by ISH products is difficult and remains a tedious and time consuming task for experts. Here we introduce a proposed framework to segment epithelial regions in oropharyngeal tissue images with ISH staining. First, we use colour deconvolution to obtain a counterstain channel and generate input patches based on superpixels and their neighbouring areas. Then, a novel deep attention residual network is applied to identify the epithelial regions to produce an epithelium segmentation mask. In the experimental results, comparing the proposed network with other state-of-the-art deep learning approaches, our network provides a better performance than region-based and pixel-based segmentations.

Item Type:	Conference or Workshop Item (Paper)
Dates:	Date Event 12 February 2019 Accepted 11 July 2019 Published Online
Uncontrolled Keywords:	automobiles, image colour analysis, image color analysis, image segmentation, deep learning, tumors, cancer, genomics
Subjects:	CAH02 - subjects allied to medicine > CAH02-05 - medical sciences > CAH02-05-01 - medical technology CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Shereen Fouad
Date Deposited:	02 Apr 2019 09:56
Last Modified:	03 Mar 2022 15:46
URI:	https://www.open-access.bcu.ac.uk/id/eprint/7311

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