ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

Singh, Vivek Kumar; Sarker, Md. Mostafa Kamal; Makhlouf, Yasmine; Craig, Stephanie G.; Humphries, Matthew P.; Loughrey, Maurice B.; James, Jacqueline A.; Salto-Tellez, Manuel; O�Reilly, Paul; Maxwell, Perry

Abstract

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.

Citation

SINGH, V.K., SARKER, M.M.K., MAKHLOUF, Y., CRAIG, S.G., HUMPHRIES, M.P., LOUGHREY, M.B., JAMES, J.A., SALTO-TELLEZ, M., O'REILLY, P. and MAXWELL, P. 2022. ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network. Cancers [online], 14(16), article 3910. Available from: https://doi.org/10.3390/cancers14163910

• Journal Article Type: Article
• Acceptance Date: Aug 11, 2022
• Online Publication Date: Aug 13, 2022
• Publication Date: Aug 31, 2022
• Deposit Date: Sep 8, 2022
• Publicly Available Date: Sep 8, 2022
• Journal: Cancers
• Electronic ISSN: 2072-6694
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 16
• Article Number: 3910
• DOI: https://doi.org/10.3390/cancers14163910
• Keywords: Colon cancer; Immunohistochemistry; ICOS; Deep learning; Channel attention
• Public URL: https://rgu-repository.worktribe.com/output/1745128