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EpiUNet: Stain-Style Transfer Model for Histology Image Based on Generative Adversarial Network

Published: 16 December 2024 Publication History

Abstract

The segmentation of tumor epithelial tissue from Hematoxylin and Eosin (HE)-stained pathology images is crucial for clinical diagnosis and quantitative analysis in oropharyngeal carcinoma (OPC). However, the heterogeneity of pathological features in OPC, where tumor epithelial tissue and other tissues such as tumor stroma exhibit similar characteristics in HE-stained images, which complicates the segmentation. While Immunohistochemistry (IHC)-stained images distinctly differentiate epithelial tissue from other tissues, they are more expensive. Hence, there is an urgent need for a technique to convert HE staining to IHC staining. To address this challenge, we propose a two-step framework consisting of stain transformation and segmentation. Firstly, a stain-style transformation model named EpiUNet is introduced to convert HE-stained images into IHC-stained images, enhancing the contrast between tumor epithelial tissue and other tissues. Secondly, a particle swarm optimization-based MLP is applied to the synthesized IHC-stained images to obtain a binary mask of OPC epithelial tissue. Experimental results demonstrate that EpiUNet generates more stable and high-fidelity images compared to other GAN-based models, achieving a binary mask accuracy of 91.84%, which surpasses current mainstream segmentation models.

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    cover image ACM Conferences
    BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
    November 2024
    614 pages
    ISBN:9798400713026
    DOI:10.1145/3698587
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    Published: 16 December 2024

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    Author Tags

    1. Convolutional neural network
    2. Oropharyngeal cancer
    3. Stain-style transfer
    4. Tissue segmentation

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    • Guangzhou Science, Technology and Innovation Commission(CN)

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    BCB '24
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    Overall Acceptance Rate 254 of 885 submissions, 29%

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