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

Published:4 October 2023 DOI: 10.1016/j.isci.2023.108145 PMID: 37867953
Lei Xu , Shichao Kan , Xiying Yu , Ye Liu , Yuxia Fu , Yiqiang Peng , Yanhui Liang , Yigang Cen , Changjun Zhu , Wei Jiang

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-Microscopy also facilitates superresolution image reconstruction from different conventional microscopic systems. The capabilities of X-Microscopy offer promising prospects for making superresolution microscopy accessible to a broader range of users, going beyond the confines of well-equipped laboratories.

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