Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging.

Abstract

Histopathological examination of tissue sections is the gold standard for disease diagnosis. However, the conventional histopathology workflow requires lengthy and laborious sample preparation to obtain thin tissue slices, causing about a one-week delay to generate an accurate diagnostic report. Recently, microscopy with ultraviolet surface excitation (MUSE), a rapid and slide-free imaging technique, has been developed to image fresh and thick tissues with specific molecular contrast. Here, we propose to apply an unsupervised generative adversarial network framework to translate colorful MUSE images into Deep-MUSE images that highly resemble hematoxylin and eosin staining, allowing easy adaptation by pathologists. By eliminating the needs of all sample processing steps (except staining), a MUSE image with subcellular resolution for a typical brain biopsy (5 mm × 5 mm) can be acquired in 5 minutes, which is further translated into a Deep-MUSE image in 40 seconds, simplifying the standard histopathology workflow dramatically and providing histological images intraoperatively.