With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining is one of the most frequently used stains for disease analysis, diagnosis, and grading, but pathologists do need different immunohistochemical (IHC) stains to analyze specific structures or cells. Obtaining all of these stains (H&E and different IHCs) on a single specimen is a tedious and time-consuming task. Consequently, virtual staining has emerged as an essential research direction. Here, we propose a novel generative model, Structural Cycle-GAN (SC-GAN), for synthesizing IHC stains from H&E images, and vice versa. Our method expressly incorporates structural information in the form of edges (in addition to color data) and employs attention modules exclusively in the decoder of the proposed generator model. This integration enhances feature localization and preserves contextual information during the generation process. In addition, a structural loss is incorporated to ensure accurate structure alignment between the generated and input markers. To demonstrate the efficacy of the proposed model, experiments are conducted with two IHC markers emphasizing distinct structures of glands in the colon: the nucleus of epithelial cells (CDX2) and the cytoplasm (CK818). Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results. Therefore, we propose two new quantitative metrics that correlate directly with the virtual staining specificity of IHC markers.

翻译：随着数字扫描仪和深度学习技术的出现，诊断操作可能从显微镜转向桌面。苏木精-伊红（H&E）染色是疾病分析、诊断和分级中最常用的染色方法之一，但病理学家确实需要不同的免疫组织化学（IHC）染色来解析特定结构或细胞。在单一标本上获取所有这些染色（H&E和不同IHC）是一项繁琐且耗时的任务。因此，虚拟染色已成为一个重要的研究方向。本文提出了一种新颖的生成模型——结构循环生成对抗网络（SC-GAN），用于从H&E图像合成IHC染色，反之亦然。我们的方法明确地将边缘形式的结构信息（除颜色数据外）纳入模型，并仅在所提出的生成器模型的解码器中采用注意力模块。这种整合增强了特征定位，并在生成过程中保留了上下文信息。此外，引入了结构损失以确保生成标记与输入标记之间的精确结构对齐。为证明该模型的有效性，我们使用两种强调结肠腺体不同结构的IHC标记进行了实验：上皮细胞核（CDX2）和细胞质（CK818）。定量指标如FID和SSIM常用于生成模型分析，但它们并未与更高质量的虚拟染色结果直接相关。因此，我们提出了两种与IHC标记虚拟染色特异性直接相关的新定量指标。