Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.

ABSTRACT: Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated di

SUBMITTER: Liu X 

PROVIDER: S-EPMC10425324 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature