Ultraviolet photoacoustic remote sensing microscopy provides label-free optical absorption contrast comparable to hematoxylin staining. This has been combined with 266 nm optical scattering microscopy offering eosin-like contrast. Here, we use unsupervised deep learning-based style transfer using the CycleGAN approach to render these pseudo-colored virtual histological images in a realistic stain style comparable to the H&E gold standard in unstained human and murine tissue specimens. A multi-pathologist diagnostic concordance study found a sensitivity of 89%, specificity of 91%, and accuracy of 90%. A blinded subjective stain quality survey suggested virtual histology was preferred over frozen sections at the 95% confidence level.
Histological analysis of tissues is the current gold standard utilized by pathologists in reviewing excised tumor specimen margins. However, as a result of time-consuming and labor-intensive pre-processing steps this approach leads to postponed post-operative feedback to surgeons resulting in worsened patient prognosis. In this work we introduce a combined ultraviolet confocal reflectance and photoacoustic remote sensing microscopy system that allows tightly optically sectioned high-resolution virtual histology imaging of freshly excised thick tissues label-free. By removing tissue preparation steps, this technique has real potential to translate post-operative feedback into the surgical suite.
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