StainBridge：跨结构染色与免疫组化染色的连续肾活检全切片图像染色感知成对配准

中文摘要 AI 辅助

组织病理学组织的三维（3D）重建需要对连续全切片图像（WSIs）进行精确的成对配准。跨染色基准已推动了不同染色组织学配准的进展，包括结构染色到免疫组化（IHC）染色对的配准，但连续肾活检切片堆栈仍然困难：它们将多种结构染色与多样的IHC标记物交错排列，这些标记物的表达可能稀疏或缺失，留下很少的共享特征可供匹配。我们提出了StainBridge，一个用于跨结构染色和IHC染色配准连续肾活检WSIs的染色感知框架。StainBridge将三个预处理组件（染色反卷积、强度归一化和组织掩膜注入）与基于XFeat的仿射初始化以及四个非刚性后端（VoxelMorph、ConvexAdam、FireANTs和DeeperHistReg）相结合。我们在23个病例（包含338张WSIs、四种结构染色和十种IHC标记物）上进行了评估，在连续切片上标注了功能性组织单元，从而在272对图像中获得了1,468个地标对应关系，并报告了组织掩膜Dice系数、以微米为单位的功能单元质心误差以及组织限制的结构相似性。非刚性细化在四个后端中的三个上改善了仿射初始化的结果，VoxelMorph是例外。DeeperHistReg自行计算初始化而非依赖XFeat，提供了最佳的合并地标精度，并配准了最多的图像对，包括所有尝试的结构-IHC对。预处理在每种染色配对类别中均改善了ConvexAdam和FireANTs的地标精度，而FireANTs在结构-IHC对上显示出最大的单一预处理增益，并具有最佳的合并组织重叠。这些结果为跨染色配准提供了实用指导，并为肾组织结构和分子表达的综合3D分析奠定了基础。

英文摘要

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.

发表机构

• University of California, Los Angeles（加州大学洛杉矶分校）
• Massachusetts General Brigham（麻省总医院布里格姆）
• Vanderbilt University（范德堡大学）
• University of Regensburg（雷根斯堡大学）
• Weill Medical College of Cornell University（康奈尔大学韦尔医学学院）
• Cornell Tech（康奈尔科技）
• Northwell Health（北岸医疗）

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