Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care.

本研究解决了现有虚拟全幻灯片图像生成方法在瓦片边界存在内容、纹理和颜色不一致的问题，这可能影响临床评估的准确性。提出的CC-WSI-Net网络利用GAN模型，集成一致性监督机制，确保生成的合成全幻灯片图像在各个瓦片之间无缝且一致。研究结果显示，该方法提升了图像质量，并为今后虚拟染色技术在研究和临床护理中的广泛应用铺平了道路。

生成无缝虚拟免疫组化全幻灯片图像的内容和颜色一致性