Super-resolved virtual staining of label-free tissue using diffusion models

• URL: http://arxiv.org/abs/2410.20073v1
• Date: Sat, 26 Oct 2024 04:31:17 GMT
• Title: Super-resolved virtual staining of label-free tissue using diffusion models
• Authors: Yijie Zhang, Luzhe Huang, Nir Pillar, Yuzhu Li, Hanlong Chen, Aydogan Ozcan,
• Abstract summary: This study presents a diffusion model-based super-resolution virtual staining approach utilizing a Brownian bridge process. Our approach integrates novel sampling techniques into a diffusion model-based image inference process. 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.
• Score: 2.8661150986074384
• License:
• Abstract: 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.

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