STAR快速精准对齐不同染色的病理切片，助力人工智能病理分析。
STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images
- 结合染色条件预处理与分层相关性匹配，实现快速刚性配准。
- 每张切片几分钟内完成配准，跨染色类型和组织重叠均稳定可靠。
- 开源轻量工具，适合临床与大规模数据准备，易复现。
连续全切片病理图像（WSI）的配准对不同染色间的直接比较及人工智能工作流（如虚拟染色、生物标志物预测）中的成对数据构建至关重要。现有方法多依赖复杂的非刚性或深度学习模型，计算开销大且难以复现，而适用于多数连续切片场景的轻量化刚性框架仍不成熟。本文提出STAR（Serial Tissue Alignment for Rigid registration），一个快速、鲁棒的开源多WSI对齐框架。STAR融合染色条件预处理与分层粗到精相关性策略、自适应核缩放及内置质量控制，可在异质组织类型和染色协议下实现可靠刚性配准，涵盖苏木精-伊红（H&E）、特殊组织化学染色（如PAS、PASM、Masson's）及免疫组化标记物（如CD31、KI67）。在涵盖多个器官和扫描条件的ANHIR 2019与ACROBAT 2022数据集上评估，STAR每张切片仅需数分钟即可实现稳定对齐，对跨染色变异和部分组织重叠具有强鲁棒性。此外，通过H&E-IHC配准、多免疫组化面板构建及典型失败案例研究，验证其实用性和局限性。作为开源轻量工具，STAR为临床应用提供可复现基线，降低门槛，支持下一代计算病理学的大规模成对数据准备。
原文摘要 · Abstract (English)
Registration of serial whole-slide histopathological images (WSIs) is critical for enabling direct comparison across diverse stains and for preparing paired datasets in artificial intelligence (AI) workflows such as virtual staining and biomarker prediction. While existing methods often rely on complex deformable or deep learning approaches that are computationally intensive and difficult to reproduce, lightweight rigid frameworks-sufficient for many consecutive-section scenarios-remain underdeveloped. We introduce STAR (Serial Tissue Alignment for Rigid registration), a fast and robust open-source framework for multi-WSI alignment. STAR integrates stain-conditioned preprocessing with a hierarchical coarse-to-fine correlation strategy, adaptive kernel scaling, and built-in quality control, achieving reliable rigid registration across heterogeneous tissue types and staining protocols, including hematoxylin-eosin (H&E), special histochemical stains (e.g., PAS, PASM, Masson's), and immunohistochemical (IHC) markers (e.g., CD31, KI67). Evaluated on the ANHIR 2019 and ACROBAT 2022 datasets spanning multiple organs and scanning conditions, STAR consistently produced stable alignments within minutes per slide, demonstrating robustness to cross-stain variability and partial tissue overlap. Beyond benchmarks, we present case studies on H&E-IHC alignment, construction of multi-IHC panels, and typical failure modes, underscoring both utility and limitations. Released as an open and lightweight tool, STAR provides a reproducible baseline that lowers the barrier for clinical adoption and enables large-scale paired data preparation for next-generation computational pathology.
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