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Histological staining of tissue biopsies, especially hematoxylin and eosin(H&E) staining, serves as the benchmark for disease diagnosis and comprehensiveclinical assessment of tissue. However, the process is laborious andtime-consuming, often limiting its usage in crucial applications such assurgical margin assessment. To address these challenges, we combine an emerging3D quantitative phase imaging technology, termed quantitative oblique backillumination microscopy (qOBM), with an unsupervised generative adversarialnetwork 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. Wedemonstrate that the approach achieves high-fidelity conversions to H&E withsubcellular detail using fresh tissue specimens from mouse liver, ratgliosarcoma, and human gliomas. We also show that the framework directlyenables additional capabilities such as H&E-like contrast for volumetricimaging. The quality and fidelity of the vH&E images are validated using both aneural network classifier trained on real H&E images and tested on virtual H&Eimages, and a user study with neuropathologists. Given its simple and low-costembodiment and ability to provide real-time feedback in vivo, this deeplearning-enabled qOBM approach could enable new workflows for histopathologywith the potential to significantly save time, labor, and costs in cancerscreening, detection, treatment guidance, and more.

Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining