K-Stain: Keypoint-Driven Correspondence for H&E-to-IHC Virtual Staining

TL;DR

K-Stain is a novel framework that uses keypoints to improve the alignment and quality of virtual IHC staining from H&E images, addressing spatial misalignment issues in tissue slices.

Contribution

We introduce K-Stain, a keypoint-driven method with hierarchical detection, enhancement, and guidance components for more accurate virtual staining.

Findings

Outperforms existing methods in quantitative metrics

Produces more visually consistent IHC images

Leverages contextual information from adjacent slices

Abstract

Virtual staining offers a promising method for converting Hematoxylin and Eosin (H&E) images into Immunohistochemical (IHC) images, eliminating the need for costly chemical processes. However, existing methods often struggle to utilize spatial information effectively due to misalignment in tissue slices. To overcome this challenge, we leverage keypoints as robust indicators of spatial correspondence, enabling more precise alignment and integration of structural details in synthesized IHC images. We introduce K-Stain, a novel framework that employs keypoint-based spatial and semantic relationships to enhance synthesized IHC image fidelity. K-Stain comprises three main components: (1) a Hierarchical Spatial Keypoint Detector (HSKD) for identifying keypoints in stain images, (2) a Keypoint-aware Enhancement Generator (KEG) that integrates these keypoints during image generation, and (3) a Keypoint Guided Discriminator (KGD) that improves the discriminator's sensitivity to spatial details. Our approach leverages contextual information from adjacent slices, resulting in more accurate and visually consistent IHC images. Extensive experiments show that K-Stain outperforms state-of-the-art methods in quantitative metrics and visual quality.