将H&E染色图像转换为IHC染色图像，提升乳腺癌诊断效率
DeReStainer: H&E to IHC Pathological Image Translation via Decoupled Staining Channels
- 分离染色通道，利用共用的苏木精通道实现精准转换
- 在BCI竞赛数据集上优于开源方法，HER2分级准确率更高
- 适合病理医生和医学影像研究者，降低诊断成本
乳腺癌是女性中最致命的癌症之一，早期检测对治疗至关重要。HER2状态作为基于免疫组化（IHC）染色的重要诊断标志物，在判断乳腺癌类型中起关键作用。由于IHC染色成本高昂，而苏木精-伊红（H&E）染色普遍使用，因此从H&E到IHC的转换具有重要意义。本文提出一种去染-重染框架，利用同一组织切片的H&E与IHC染色共享苏木精通道的特点，设计针对苏木精和二氨基联苯胺（DAB）通道的专用损失函数，以更好地生成IHC图像。除基准评估外，还引入了用于评估HER2水平的语义信息指标。实验结果表明，该方法在图像内在属性和语义信息方面均优于现有开源方法。
原文摘要 · Abstract (English)
Breast cancer is a highly fatal disease among cancers in women, and early detection is crucial for treatment. HER2 status, a valuable diagnostic marker based on Immunohistochemistry (IHC) staining, is instrumental in determining breast cancer status. The high cost of IHC staining and the ubiquity of Hematoxylin and Eosin (H&E) staining make the conversion from H&E to IHC staining essential. In this article, we propose a destain-restain framework for converting H&E staining to IHC staining, leveraging the characteristic that H&E staining and IHC staining of the same tissue sections share the Hematoxylin channel. We further design loss functions specifically for Hematoxylin and Diaminobenzidin (DAB) channels to generate IHC images exploiting insights from separated staining channels. Beyond the benchmark metrics on BCI contest, we have developed semantic information metrics for the HER2 level. The experimental results demonstrated that our method outperforms previous open-sourced methods in terms of image intrinsic property and semantic information.
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