Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining

• Published in: Photoacoustics (Munich) Vol. 25; p. 100308
• Main Authors: Kang, Lei, Li, Xiufeng, Zhang, Yan, Wong, Terence T.W.
• Format: Journal Article
• Language: English
• Published: Germany Elsevier GmbH 01.03.2022; Elsevier
• Subjects: Deep learning; Histological imaging; Photoacoustic microscopy; Unsupervised learning; Deep learning; Histological imaging; Photoacoustic microscopy; Unsupervised learning
• ISSN: 2213-5979; 2213-5979
• DOI: 10.1016/j.pacs.2021.100308

Histological images can reveal rich cellular information of tissue sections, which are widely used by pathologists in disease diagnosis. However, the gold standard for histopathological examination is based on thin sections on slides, which involves inevitable time-consuming and labor-intensive tissue processing steps, hindering the possibility of intraoperative pathological assessment of the precious patient specimens. Here, by incorporating ultraviolet photoacoustic microscopy (UV-PAM) with deep learning, we show a rapid and label-free histological imaging method that can generate virtually stained histological images (termed Deep-PAM) for both thin sections and thick fresh tissue specimens. With the tissue non-destructive nature of UV-PAM, the imaged intact specimens can be reused for other ancillary tests. We demonstrated Deep-PAM on various tissue preparation protocols, including formalin-fixation and paraffin-embedding sections (7-µm thick) and frozen sections (7-µm thick) in traditional histology, and rapid assessment of intact fresh tissue (~ 2-mm thick, within 15 min for a tissue with a surface area of 5 mm × 5 mm). Deep-PAM potentially serves as a comprehensive histological imaging method that can be simultaneously applied in preoperative, intraoperative, and postoperative disease diagnosis.