Abstract
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.
Abstract (translated)
免疫组化（IHC）染色在病理学家分析医学影像中起着关键作用，为各种疾病提供重要诊断信息。从HE染色 whole slide images（WSIs）中进行虚拟染色允许在没有昂贵的物理染色过程的情况下自动生成其他有用的IHC染色。然而，基于块处理的方法生成的虚拟WSI通常在内容、纹理和颜色在块边界处存在不稳定性。这些不稳定性导致伪影，可能影响准确临床评估和诊断。为了克服这一局限，我们提出了一种新颖的CC-WSI合成网络，将GAN模型扩展以产生无缝的合成整张图片。我们的CC-WSI-Net集成了一个内容和服务器，确保跨块的一致性，并在保证Sox10免疫组化精度的 melanocyte检测的同时，促进无缝合成WSIs。我们对我们的方法通过大量的图像质量分析、客观检测评估和病理学家主观调查进行了验证。通过生成高质量的合成WSIs，我们的方法为在研究和临床护理中应用更广泛的虚拟染色技术打开了大门。
URL
https://arxiv.org/abs/2410.01072
