Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy.

ABSTRACT: The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm², at resolution equivalent to 400x digital histopathology. Quantitative validation suggests strong concordance with conventional histology in benign and malignant prosta

SUBMITTER: Martell MT 

PROVIDER: S-EPMC10519961 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature