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. (In Press)

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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)
Subjects: B800 Medical Technology
G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
REF UoA Output Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Shereen Fouad
Date Deposited: 02 Apr 2019 09:56
Last Modified: 02 Apr 2019 09:56
URI: http://www.open-access.bcu.ac.uk/id/eprint/7311

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