Virtual birefringence imaging and histological staining of amyloid
  deposits in label-free tissue using autofluorescence microscopy and deep
  learning
Xilin Yang Bijie Bai Yijie Zhang Musa Aydin Sahan Yoruc Selcuk Zhen Guo Gregory A. Fishbein Karine Atlan William Dean Wallace Nir Pillar Aydogan Ozcan
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.