Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency
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arXiv
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| Format: | Preprint |
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2024
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| author | Liu, Sitong Liu, Kechun Margolis, Samuel Wu, Wenjun Knezevich, Stevan R. Elder, David E Eguchi, Megan M. Elmore, Joann G Shapiro, Linda |
| author_facet | Liu, Sitong Liu, Kechun Margolis, Samuel Wu, Wenjun Knezevich, Stevan R. Elder, David E Eguchi, Megan M. Elmore, Joann G Shapiro, Linda |
| contents | Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency Liu, Sitong Liu, Kechun Margolis, Samuel Wu, Wenjun Knezevich, Stevan R. Elder, David E Eguchi, Megan M. Elmore, Joann G Shapiro, Linda Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care. |
| title | Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2410.01072 |