Pixel super-resolved virtual staining of label-free tissue using diffusion models

Yijie Zhang, Luzhe Huang, Nir Pillar, Yuzhu Li, Hanlong Chen and Aydogan Ozcan ()
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Yijie Zhang: University of California
Luzhe Huang: University of California
Nir Pillar: University of California
Yuzhu Li: University of California
Hanlong Chen: University of California
Aydogan Ozcan: University of California

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: 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 pixel 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 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 pixel super-resolution virtual staining model consistently outperforms conventional approaches in resolution, structural similarity and perceptual accuracy, successfully achieving a pixel super-resolution factor of 4-5×, increasing the output space-bandwidth product by 16-25-fold compared to the input label-free microscopy images. Diffusion-based pixel 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.

Date: 2025
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DOI: 10.1038/s41467-025-60387-z

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