By combining an imaging modality (Ultraviolet Photoacoustic Microscopy) with deep learning (unpaired image-to-image translation network), we demonstrate a digital histological imaging method that can acquire the virtual histological images of both thin and thick label-free tissue specimens. Its capability to directly get the virtual histological images of fresh tissue will cut off the laborious tissue preprocessing procedures for traditional histological staining, thus greatly expediting the histological examination for pathologists. In our method, the cycle-consistent adversarial network (Cycle-GAN) is trained to transform the ultraviolet photoacoustic microscopy (UV-PAM) images of label-free tissue to bright-field microscopy images of hematoxylin- and eosin-stained tissue sections. Th...[ Read more ]

By combining an imaging modality (Ultraviolet Photoacoustic Microscopy) with deep learning (unpaired image-to-image translation network), we demonstrate a digital histological imaging method that can acquire the virtual histological images of both thin and thick label-free tissue specimens. Its capability to directly get the virtual histological images of fresh tissue will cut off the laborious tissue preprocessing procedures for traditional histological staining, thus greatly expediting the histological examination for pathologists. In our method, the cycle-consistent adversarial network (Cycle-GAN) is trained to transform the ultraviolet photoacoustic microscopy (UV-PAM) images of label-free tissue to bright-field microscopy images of hematoxylin- and eosin-stained tissue sections. The networks can be trained using unpaired images of UV-PAM label-free tissue images and bright-field microscopy histologically stained tissue images from different tissue sections, which means our method can bypass all the difficulty of acquiring paired images datasets for supervised network training. Compared with other deep learning-based staining methods, we have advanced the deep learning’s application by successfully applying unpaired image-to-image translation methods such as Cycle-GAN in our staining method and successfully achieved the virtual histological staining of thick un-sectioned tissue. We showed the effectiveness of the UV-PAM based virtual histological imaging method on both thin mouse brain sections and thick fresh mouse brain.