Multi-target stain normalization for histology slides
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910478980087808 |
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| author | Ivanov, Desislav Barbano, Carlo Alberto Grangetto, Marco |
| author_facet | Ivanov, Desislav Barbano, Carlo Alberto Grangetto, Marco |
| contents | Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02077 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-target stain normalization for histology slides Ivanov, Desislav Barbano, Carlo Alberto Grangetto, Marco Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 68U10 I.4.0 Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In this study, we introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes. We evaluate the effectiveness of our method through experiments using a deep-learning pipeline for automatic nuclei segmentation on colorectal images. Our results show that by leveraging multiple reference images, better results can be achieved when generalizing to external data, where the staining can widely differ from the training set. |
| title | Multi-target stain normalization for histology slides |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 68U10 I.4.0 |
| url | https://arxiv.org/abs/2406.02077 |