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.

[img]
Preview
Text
Paper_092418_ISBI.pdf - Accepted Version

Download (232kB)

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:
DateEvent
12 February 2019Accepted
11 July 2019Published 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 > School of Computing and Digital Technology
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

Actions (login required)

View Item View Item

Research

In this section...