Deep learning enables stochastic optical reconstruction microscopy-like superresolution image reconstruction from conventional microscopy.

ABSTRACT: Despite its remarkable potential for transforming low-resolution images, deep learning faces significant challenges in achieving high-quality superresolution microscopy imaging from wide-field (conventional) microscopy. Here, we present X-Microscopy, a computational tool comprising two deep learning subnets, UR-Net-8 and X-Net, which enables STORM-like superresolution microscopy image reconstruction from wide-field images with input-size flexibility. X-Microscopy was trained using samples of various subcellular structures, including cytoskeletal filaments, dot-like, beehive-like, and nanocluster-like structures, to generate prediction models capable of producing images of comparable quality to STORM-like images. In addition to enabling multicolour superresolution image reconstructions, X-M

SUBMITTER: Xu L 

PROVIDER: S-EPMC10587619 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature