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.
|Title of host publication
|ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
|Number of pages
|Published - 11 Jul 2019
|16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 2019 → 11 Apr 2019
|Proceedings - International Symposium on Biomedical Imaging
|16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
|8/04/19 → 11/04/19
Bibliographical noteFunding Information:
This work was supported by the EPSRC (UK) through funding under grant EP/M023869/1 Novel context-based segmentation algorithms for intelligent microscopy”.
- Deep learning
- In situ hybridisation
- Oropharyngeal cancer
- Tumor segmentation