无需标注即可快速评估多染色病理切片配准质量
Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs
- 结合组织掩膜与形变特征进行无监督评估
- 与人工评估高度相关，实时且低资源消耗
- 适合大规模数字病理质量控制场景
高保真配准病理全幻灯片图像（如H&E和IHC）对整合分子分析至关重要，但缺乏真实标注时难以评估。现有基于地标或强度相似性的全片级评估方法通常耗时、不可靠且计算开销大，限制了大规模应用。本研究提出一种快速、无监督的框架，联合使用下采样组织掩膜与形变特征指标，对注册后的H&E与IHC全幻灯片对进行配准质量评估（RQA）。掩膜指标衡量全局结构对应性，形变指标评估局部平滑性、连续性与变换真实性。在多个IHC标志物及多位专家评估中验证显示，自动指标与人工评价高度相关。在无真实标注条件下，该框架提供高保真、实时的RQA，仅需极少计算资源，适用于数字病理的大规模质量控制。
原文摘要 · Abstract (English)
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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