快速、无监督的多染色组织全切片图像配准质量评估框架
Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs
AI总结：
本文提出一种快速无监督框架，用于评估多染色全切片图像配准质量，通过结合掩码和变形度量指标，实现高保真度的配准质量评估。
摘要：
高保真的病理全切片图像（WSI）配准，如苏木精伊红（H&E）和免疫组化（IHC）图像，对于整合分子分析至关重要，但缺乏地面真实（GT）标注时评估极具挑战性。现有WSI级评估方法——使用标注地标或基于强度的相似性度量——往往耗时、不可靠且计算密集，限制了大规模应用。本研究提出了一种快速、无监督的框架，联合使用下采样组织掩码和变形度量指标，用于评估已配准的H&E和IHC WSI配对的配准质量（RQA）。掩码基于指标测量全局结构对应性，变形基于指标评估局部平滑度、连续性和变换真实性。在多个IHC标记和多专家评估中，验证了自动化指标与人类评估之间的强相关性。在无GT的情况下，该框架提供了可靠的、实时的RQA，具有高保真度和最小的计算资源，使其适用于数字病理学中的大规模质量控制。
英文摘要：
High-fidelity registration of histopathological whole slide images (WSIs), such as hematoxylin & eosin (H&E) and immunohistochemistry (IHC), is vital for integrated molecular analysis but challenging to evaluate without ground-truth (GT) annotations. Existing WSI-level assessments -- using annotated landmarks or intensity-based similarity metrics -- are often time-consuming, unreliable, and computationally intensive, limiting large-scale applicability. This study proposes a fast, unsupervised framework that jointly employs down-sampled tissue masks- and deformations-based metrics for registration quality assessment (RQA) of registered H&E and IHC WSI pairs. The masks-based metrics measure global structural correspondence, while the deformations-based metrics evaluate local smoothness, continuity, and transformation realism. Validation across multiple IHC markers and multi-expert assessments demonstrate a strong correlation between automated metrics and human evaluations. In the absence of GT, this framework offers reliable, real-time RQA with high fidelity and minimal computational resources, making it suitable for large-scale quality control in digital pathology.
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