OT-StainNet: Optimal Transport Driven Semantic Matching for Weakly Paired H&E-to-IHC Stain Transfer

Abstract

Immunohistochemistry (IHC) examination is essential for characterizing tumor subtypes, providing prognostic information, and developing personalized treatment plans. However, IHC staining preparation is more complex and expensive compared to Hematoxylin and Eosin (H&E) staining, limiting its widespread clinical application. Transforming H&E images into IHC images presents a promising solution. In this paper, we propose OT-StainNet, a novel virtual IHC staining method. OT-StainNet employs a pre-trained diffusion model with richer prior knowledge as the generator and fine-tunes it with LoRA adapters through adversarial training. Given that adjacent images of the same tissue stained with H&E and IHC are not precisely aligned at the pixel level, existing methods struggle to fully utilize the supervisory information from weakly paired IHC images. To address this issue, we propose an optimal transport-driven semantic matching (OTSM) mechanism, establishing accurate semantic correspondences between H&E-IHC image pairs. By leveraging the real IHC features obtained through the OTSM mechanism, we design a semantic consistency constraint (SCC) to ensure that the correlations among virtual IHC features remain consistent with those among real IHC features, thereby preserving valuable correlation information during stain transfer. We validate OT-StainNet using four types of IHC staining across two datasets. Extensive experiments demonstrate the effectiveness of our method compared to state-of-the-art approaches.