Systemic amyloidosis is a group of diseases characterized by the deposition
of misfolded proteins in various organs and tissues, leading to progressive
organ dysfunction and failure. Congo red stain is the gold standard chemical
stain for the visualization of amyloid deposits in tissue sections, as it forms
complexes with the misfolded proteins and shows a birefringence pattern under
polarized light microscopy. However, Congo red staining is tedious and costly
to perform, and prone to false diagnoses due to variations in the amount of
amyloid, staining quality and expert interpretation through manual examination
of tissue under a polarization microscope. Here, we report the first
demonstration of virtual birefringence imaging and virtual Congo red staining
of label-free human tissue to show that a single trained neural network can
rapidly transform autofluorescence images of label-free tissue sections into
brightfield and polarized light microscopy equivalent images, matching the
histochemically stained versions of the same samples. We demonstrate the
efficacy of our method with blind testing and pathologist evaluations on
cardiac tissue where the virtually stained images agreed well with the
histochemically stained ground truth images. Our virtually stained polarization
and brightfield images highlight amyloid birefringence patterns in a
consistent, reproducible manner while mitigating diagnostic challenges due to
variations in the quality of chemical staining and manual imaging processes as
part of the clinical workflow.

通过使用训练有素的神经网络，我们首次展示了对无标签人体组织的虚拟双折射成像和虚拟刚果红染色，快速地将自发荧光图像转化为同一样本的亮场显微镜和偏振光显微镜等效图像，与同一样本的组织化学染色版本相匹配。我们的方法在心脏组织上进行了实验证实和病理学家评估，虚拟染色的结果与组织化学染色的真实图像吻合，强调了淀粉样物质的双折射模式，从而减少了临床工作流程中与化学染色和手动成像过程质量变异相关的诊断挑战。