2026-01-12 カリフォルニア工科大学(Caltech)
From left to right: Images of kidney tissue as detected with UV-PAM, as imaged by AI to mimic traditional H&E staining, and as they appear when directly treated with H&E staining.
<関連情報>
- https://www.caltech.edu/about/news/intraoperative-tumor-histology
- https://www.science.org/doi/10.1126/sciadv.adz1820
ディープラーニングを活用したラベルフリーの細胞内解像度光音響組織学を用いた迅速な癌診断 Rapid cancer diagnosis using deep learning–powered label-free subcellular-resolution photoacoustic histology
Byullee Park, Rui Cao, Yilin Luo, Cindy Liu, […] , and Lihong V. Wang
Science Advances 21 Nov 2025
DOI:https://doi.org/10.1126/sciadv.adz1820
Abstract
Traditional hematoxylin and eosin staining in formalin-fixed paraffin-embedded sections, while essential for diagnostic pathology, is time-consuming, labor intensive, and prone to artifacts that can obscure critical histological details. Label-free ultraviolet photoacoustic microscopy (UV-PAM) has emerged as a promising alternative, offering fast histology-like images without the need for traditional staining and excessive tissue preparation. However, current UV-PAM systems face challenges in achieving the high spatial resolution required for detailed histological analysis and diagnosis. To address this, we developed a subcellular-resolution UV-PAM (SRUV-PAM) system with a 240-nanometer resolution, enabled by the integration of a high numerical aperture (NA) objective lens (NA = 0.64) and the precise piezo actuators for fine scanning control. This configuration allows visualization of detailed nuclear structures. In addition, we demonstrated virtual staining of SRUV-PAM images via cycle-consistent generative adversarial networks and diagnosis of malignant and benign tumors in liver tissues via densely connected convolutional networks DenseNet-121, achieving an area under the receiver operating characteristic curve of 0.902.
