Pathological Semantics-Preserving Learning for H&E-to-IHC Virtual Staining

TL;DR

This paper introduces PSPStain, a novel method for virtual H&E-to-IHC staining that preserves pathological semantics and improves molecular-level accuracy despite spatial misalignments.

Contribution

It proposes two innovative learning strategies, PALS and PCLS, to incorporate semantic information and enhance interaction for more accurate virtual staining.

Findings

Outperforms existing H&E-to-IHC virtual staining methods

Achieves high pathological correlation with real stains

Excels in clinical and image quality metrics

Abstract

Conventional hematoxylin-eosin (H&E) staining is limited to revealing cell morphology and distribution, whereas immunohistochemical (IHC) staining provides precise and specific visualization of protein activation at the molecular level. Virtual staining technology has emerged as a solution for highly efficient IHC examination, which directly transforms H&E-stained images to IHC-stained images. However, virtual staining is challenged by the insufficient mining of pathological semantics and the spatial misalignment of pathological semantics. To address these issues, we propose the Pathological Semantics-Preserving Learning method for Virtual Staining (PSPStain), which directly incorporates the molecular-level semantic information and enhances semantics interaction despite any spatial inconsistency. Specifically, PSPStain comprises two novel learning strategies: 1) Protein-Aware Learning Strategy (PALS) with Focal Optical Density (FOD) map maintains the coherence of protein expression level, which represents molecular-level semantic information; 2) Prototype-Consistent Learning Strategy (PCLS), which enhances cross-image semantic interaction by prototypical consistency learning. We evaluate PSPStain on two public datasets using five metrics: three clinically relevant metrics and two for image quality. Extensive experiments indicate that PSPStain outperforms current state-of-the-art H&E-to-IHC virtual staining methods and demonstrates a high pathological correlation between the staging of real and virtual stains.