Topology-aware Diffusion Schrödinger Bridge for Unpaired H&E-to-IHC Stain Translation

Unpaired H&E-to-IHC Stain Translation aims to generate immunohistochemistry (IHC) staining from Hematoxylin and Eosin (H&E) staining. It offers clearer diagnostic insights and potentially expands access to advanced pathology services in resource-limited areas. This task faces two primary challenges: capturing target domain style characteristics and preserving topological features in histological images. Recently, Schrödinger Bridge (SB)-based methods have offered a solution for unpaired image-to-image translation, addressing the mode collapse and artifact issues in CycleGAN-based approaches, as well as the Gaussian prior assumption limitation in diffusion-based methods. While SB-based methods suffer from the curse of dimensionality with high-resolution images, the Unpaired Neural Schrödinger Bridge (UNSB) overcomes this challenge and achieves state-of-the-art (SOTA) performance on natural images. However, UNSB has two key issues in histological images: (1) loss of topological features and (2) IHC staining representation. UNSB focuses only on the optimal path from source to target domains, ignoring local structure paths. Convolutional neural networks (CNNs) do not perfectly preserve critical anatomical structures due to limitations like receptive field size or model capacity. To address these challenges, we introduce the Topology-aware Diffusion Schrödinger Bridge (TDSB), integrating a Topology Guidance (TG) module and Dual-Domain Adaptive Patch-based noise contrastive estimation (DDAP). Experiments on seven translation tasks across three datasets show that our method achieves SOTA performance in unpaired H&E-to-IHC stain translation. Clinical evaluation through pathologists' assessments further validates the effectiveness of our method.