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

Journal: Nature communications
Published Date: May 30, 2025

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.

Authors

  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Luzhe Huang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.
  • Nir Pillar
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Hanlong Chen
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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