用扩散模型提升无标记组织图像的分辨率与染色效果。
Pixel super-resolved virtual staining of label-free tissue using diffusion models
- 基于扩散模型和布朗桥采样，提升虚拟染色稳定性。
- 实现4-5倍超分辨率，空间带宽积提升16-25倍。
- 适合临床病理诊断中无需化学染色的高精度成像需求。
虚拟染色可将未染色组织的无标记显微图像转换为类似组织化学染色的效果。本研究提出一种基于扩散模型的超分辨率虚拟染色方法，利用布朗桥过程提升图像的空间分辨率和保真度，克服传统深度学习方法的局限。通过在扩散模型图像生成过程中引入新型采样技术，显著降低生成图像的方差，使输出更稳定、准确。该方法直接应用于低分辨率的人类肺组织自荧光图像，结果表明，在分辨率、结构相似性和感知准确性方面均优于传统方法，实现了4-5倍超分辨率，输出空间带宽积比输入图像提高16-25倍。基于扩散模型的超分辨率虚拟染色不仅提升了图像质量，还增强了无化学染色条件下虚拟染色的可靠性，对临床诊断具有重要应用潜力。
原文摘要 · Abstract (English)
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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