MCS-Stain: Boosting FFPE-to-HE Virtual Staining With Multiple Cell Semantics.

Abstract

The diagnosis of cancer primarily relies on pathological slides stained with hematoxylin and eosin (HE). These slides are typically prepared from tissue samples that have been fixed in formalin and embedded in paraffin (FFPE). However, the traditional process of staining FFPE samples with HE is time-consuming and resource-intensive. Recent advances in virtual staining technologies, driven by digital pathology and generative models, offer a promising alternative. However, the blurred structures in FFPE images pose unique challenges to achieving high-quality FFPE-to-HE virtual staining. In this context, we developed a novel Multiple Cell Semantics-guided supervised generative adversarial model, MCS-Stain. Specifically, the guidance consists of three components: 1) pretrained cell semantic guidance, aligning the powerful intermediate features of real and virtual images, embedded in the pretrained cell segmentation model (PCSM); 2) cell mask guidance, introducing comprehensible cell information which serves as part of the input to the discriminator through channel concatenation; 3) dynamic cell semantic guidance, aligning the dynamic intermediate features embedded in the generator during training. The comparative results on FFPE-to-HE datasets demonstrated that MCS-Stain outperforms existing state-of-the-art (SOTA) methods with substantial qualitative and quantitative improvements. Results across various PCSMs and data sources further confirmed its effectiveness and robustness. Notably, the dynamic cell semantic exhibits strong potential beyond FFPE-to-HE virtual staining, further demonstrated by virtual staining from HE images to immunohistochemical (IHC) images. In general, MCS-Stain presents a promising avenue to advance virtual staining techniques. Code is available at https://github.com/huyihuang/MCS-Stain.