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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2602.04046 |
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| _version_ | 1866910011021590528 |
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| author | Dubey, Shikha Raciti, Patricia Standish, Kristopher Ramon, Albert Juan Burlingame, Erik Ames |
| author_facet | Dubey, Shikha Raciti, Patricia Standish, Kristopher Ramon, Albert Juan Burlingame, Erik Ames |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04046 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs Dubey, Shikha Raciti, Patricia Standish, Kristopher Ramon, Albert Juan Burlingame, Erik Ames Computer Vision and Pattern Recognition 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. |
| title | Fast, Unsupervised Framework for Registration Quality Assessment of Multi-stain Histological Whole Slide Pairs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.04046 |