StainBridge: Stain-Aware Pairwise Registration of Serial Renal Biopsy Whole-Slide Images Across Structural and Immunohistochemical Stains

Abstract

Three-dimensional (3D) reconstruction of histopathology tissue requires accurate pairwise registration of serial whole-slide images (WSIs). Cross-stain benchmarks have advanced registration of differently stained histology, including structural-to-immunohistochemistry (IHC) pairs, but serial renal biopsy stacks remain difficult: they interleave several structural stains with diverse IHC markers whose expression can be sparse or absent, leaving few shared features to match. We present StainBridge, a stain-aware framework for registering serial renal biopsy WSIs across structural and IHC stains. StainBridge couples three preprocessing components, stain deconvolution, intensity normalization, and tissue-mask injection, with XFeat-based affine initialization and four nonrigid backends (VoxelMorph, ConvexAdam, FireANTs, and DeeperHistReg). We evaluate it on 23 cases comprising 338 WSIs, four structural stains, and ten IHC markers, with functional tissue units annotated on consecutive sections to give 1,468 landmark correspondences across 272 image pairs, and report tissue-mask Dice, functional-unit centroid error in micrometers, and tissue-restricted structural similarity. Nonrigid refinement improves on the affine initialization for three of four backends, VoxelMorph being the exception. DeeperHistReg, which computes its own initialization rather than relying on XFeat, gives the best pooled landmark accuracy and registers the most pairs, including every attempted structural-IHC pair. Preprocessing improves landmark accuracy for ConvexAdam and FireANTs in every stain-pairing category, and FireANTs shows both the largest single preprocessing gain on structural-IHC pairs and the best pooled tissue overlap. These results offer practical guidance for cross-stain registration and a foundation for integrated 3D analysis of renal tissue architecture and molecular expression.