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 language | English |
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Title of host publication | 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1527-1531 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-3640-4 |
DOIs | |
Publication status | Published - 11 Jul 2019 |
Event | 2019 IEEE 16th International Symposium on Biomedical Imaging - Venice, Italy Duration: 8 Apr 2019 → 11 Apr 2019 https://biomedicalimaging.org/2019/ |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2019-April |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 2019 IEEE 16th International Symposium on Biomedical Imaging |
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Abbreviated title | ISBI 2019 |
Country/Territory | Italy |
City | Venice |
Period | 8/04/19 → 11/04/19 |
Internet address |
Keywords
- oropharyngeal cancer
- tumor segmentation
- deep learning
- in situ hybridisation
- histology