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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes

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.

Details

Original languageEnglish
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Publication statusPublished - 11 Jul 2019
Event2019 IEEE 16th International Symposium on Biomedical Imaging - Venice, Italy
Duration: 8 Apr 201911 Apr 2019
https://biomedicalimaging.org/2019/

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2019 IEEE 16th International Symposium on Biomedical Imaging
Abbreviated titleISBI 2019
CountryItaly
CityVenice
Period8/04/1911/04/19
Internet address

Keywords

  • oropharyngeal cancer, tumor segmentation, deep learning, in situ hybridisation, histology