NIR-II Fluorescence Image Translation via Latent Space Disentanglement

The second near-infrared window (NIR-II) fluorescence imaging is an excellent optical in vivo imaging method. Compared with NIR-IIa window (1000–1300 nm), NIR-IIb window (1500–1700 nm) imaging can significantly improve the imaging effect. However, due to the limitation that there are no molecular probes approved for NIR-IIb imaging in humans, we expect to achieve the translation of NIR-IIa images to NIR-IIb images through artificial intelligence. NIR-II fluorescence imaging is divided into macroscopic imaging of animal bodies and microscopic imaging of tissue and nerves. The two imaging scenarios are different. To realize the translation of two scene images at the same time, this paper designs a generative adversarial network model. The core idea is to disentangle the information in the encoded latent space into the information shared by the macroscopic and microscopic images and information specific to both to extract the high-quality feature maps for decoding. In addition, we improve the contrastive loss and use the attention-aware sampling strategy to select patches, which further maintains the source image content structure. The experiment results demonstrate the superiority and effectiveness of the proposed method.