TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus Infected Cells in Histopathological Images
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85163382874
Journal Title
8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023
Start Page
238
End Page
243
Rights Holder(s)
SCOPUS
Bibliographic Citation
8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023 (2023) , 238-243
Suggested Citation
Sermpanichakij W., Jitkongchuen D., Prasertchai T. TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus Infected Cells in Histopathological Images. 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023 (2023) , 238-243. 243. doi:10.1109/ECTIDAMTNCON57770.2023.10139588 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87858
Title
TransEN U-Net: Enhance Image Segmentation of Cytomegalovirus Infected Cells in Histopathological Images
Author(s)
Author's Affiliation
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Abstract
Advances in histopathological image segmentation have a significant role in the diagnosis and begin treatment immediately including a study of Cytomegalovirus(CMV) for the tissues. Histopathological change with confirmation by immuno-histochemical or in situ hybridization study is a gold standard for diagnosis of CMV tissue infection. This required pathologists to identify the histopathological change which is time-consuming and can be missed in tissue with a subtle change. Automatic analysis of histopathological images with Deep Learning(DL) can help pathologists to diagnose CMV tissue infection more accurately. Typical issues with histopathological images which impede automatic analysis are the extremely enormous size of histopathological images more than 1 gigapixel, the limitations of GPU memory, and a limited number of histopathology images. Additionally, whole slide histopathological images are split huge images into multiple small image patches by cropping using the sliding window technique. In this paper, we propose TransEN U-Net which derives a benefit of a hybrid CNN-Transformer base on the U-shaped architecture for boosting the performance of segmentation of histopathology. The transformer encoder not only is able to the patches but also the relative self-attention mechanism in order to share information between sequences. Experiment results of segmenting images by the two-dimensional indicate that the TransEN U-Net can productively discriminate CMV viral inclusions including achieving higher values in terms of DSC score.