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

Tzu Hsi Song, Gabriel Landini, Shereen Fouad, Hisham Mehanna

Research output: Chapter in Book/Published conference outputConference publication

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.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE
Pages1527-1531
Number of pages5
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - 11 Jul 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

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

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period8/04/1911/04/19

Bibliographical note

Funding Information:
This work was supported by the EPSRC (UK) through funding under grant EP/M023869/1 Novel context-based segmentation algorithms for intelligent microscopy”.

Keywords

  • Deep learning
  • Histology
  • In situ hybridisation
  • Oropharyngeal cancer
  • Tumor segmentation

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