STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916930829418496 |
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| author | Liu, Zeyu Ding, Shengwei |
| author_facet | Liu, Zeyu Ding, Shengwei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_02952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images Liu, Zeyu Ding, Shengwei Computer Vision and Pattern Recognition 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. |
| title | STAR: A Fast and Robust Rigid Registration Framework for Serial Histopathological Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.02952 |