  Histopathological visualizations are a pillar of modern medicine and
biological research. Surgical oncology relies exclusively on post-operative
histology to determine definitive surgical success and guide adjuvant
treatments. The current histology workflow is based on bright-field microscopic
assessment of histochemical stained tissues and has some major limitations. For
example, the preparation of stained specimens for brightfield assessment
requires lengthy sample processing, delaying interventions for days or even
weeks. Hence, there is a pressing need for improved histopathology methods. In
this paper, we present a deep-learning-based approach for virtual label-free
histochemical staining of total-absorption photoacoustic remote sensing
(TA-PARS) images of unstained tissue. TA-PARS provides an array of directly
measured label-free contrasts such as scattering and total absorption
(radiative and non-radiative), ideal for developing H&E colorizations without
the need to infer arbitrary tissue structures. We use a Pix2Pix generative
adversarial network (GAN) to develop visualizations analogous to H&E staining
from label-free TA-PARS images. Thin sections of human skin tissue were first
virtually stained with the TA-PARS, then were chemically stained with H&E
producing a one-to-one comparison between the virtual and chemical staining.
The one-to-one matched virtually- and chemically- stained images exhibit high
concordance validating the digital colorization of the TA-PARS images against
the gold standard H&E. TA-PARS images were reviewed by four dermatologic
pathologists who confirmed they are of diagnostic quality, and that resolution,
contrast, and color permitted interpretation as if they were H&E. The presented
approach paves the way for the development of TA-PARS slide-free histology,
which promises to dramatically reduce the time from specimen resection to
histological imaging.
