  Histological staining of tissue biopsies, especially hematoxylin and eosin
(H&E) staining, serves as the benchmark for disease diagnosis and comprehensive
clinical assessment of tissue. However, the process is laborious and
time-consuming, often limiting its usage in crucial applications such as
surgical margin assessment. To address these challenges, we combine an emerging
3D quantitative phase imaging technology, termed quantitative oblique back
illumination microscopy (qOBM), with an unsupervised generative adversarial
network pipeline to map qOBM phase images of unaltered thick tissues (i.e.,
label- and slide-free) to virtually stained H&E-like (vH&E) images. We
demonstrate that the approach achieves high-fidelity conversions to H&E with
subcellular detail using fresh tissue specimens from mouse liver, rat
gliosarcoma, and human gliomas. We also show that the framework directly
enables additional capabilities such as H&E-like contrast for volumetric
imaging. The quality and fidelity of the vH&E images are validated using both a
neural network classifier trained on real H&E images and tested on virtual H&E
images, and a user study with neuropathologists. Given its simple and low-cost
embodiment and ability to provide real-time feedback in vivo, this deep
learning-enabled qOBM approach could enable new workflows for histopathology
with the potential to significantly save time, labor, and costs in cancer
screening, detection, treatment guidance, and more.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Medical Physics;Quantitative Methods