Pixel super-resolved virtual staining of label-free tissue using diffusion models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908427889934336 |
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| author | Zhang, Yijie Huang, Luzhe Pillar, Nir Li, Yuzhu Chen, Hanlong Ozcan, Aydogan |
| author_facet | Zhang, Yijie Huang, Luzhe Pillar, Nir Li, Yuzhu Chen, Hanlong Ozcan, Aydogan |
| contents | Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process to enhance both the spatial resolution and fidelity of label-free virtual tissue staining, addressing the limitations of traditional deep learning-based methods. Our approach integrates novel sampling techniques into a diffusion model-based image inference process to significantly reduce the variance in the generated virtually stained images, resulting in more stable and accurate outputs. Blindly applied to lower-resolution auto-fluorescence images of label-free human lung tissue samples, the diffusion-based super-resolution virtual staining model consistently outperformed conventional approaches in resolution, structural similarity and perceptual accuracy, successfully achieving a super-resolution factor of 4-5x, increasing the output space-bandwidth product by 16-25-fold compared to the input label-free microscopy images. Diffusion-based super-resolved virtual tissue staining not only improves resolution and image quality but also enhances the reliability of virtual staining without traditional chemical staining, offering significant potential for clinical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pixel super-resolved virtual staining of label-free tissue using diffusion models Zhang, Yijie Huang, Luzhe Pillar, Nir Li, Yuzhu Chen, Hanlong Ozcan, Aydogan Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics Optics Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process to enhance both the spatial resolution and fidelity of label-free virtual tissue staining, addressing the limitations of traditional deep learning-based methods. Our approach integrates novel sampling techniques into a diffusion model-based image inference process to significantly reduce the variance in the generated virtually stained images, resulting in more stable and accurate outputs. Blindly applied to lower-resolution auto-fluorescence images of label-free human lung tissue samples, the diffusion-based super-resolution virtual staining model consistently outperformed conventional approaches in resolution, structural similarity and perceptual accuracy, successfully achieving a super-resolution factor of 4-5x, increasing the output space-bandwidth product by 16-25-fold compared to the input label-free microscopy images. Diffusion-based super-resolved virtual tissue staining not only improves resolution and image quality but also enhances the reliability of virtual staining without traditional chemical staining, offering significant potential for clinical diagnostics. |
| title | Pixel super-resolved virtual staining of label-free tissue using diffusion models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Medical Physics Optics |
| url | https://arxiv.org/abs/2410.20073 |