摘要
A fully convolutional encoder-decoder network(FCEDN),a deep learning model,was developed and applied to image scanning microscopy(ISM).Super-resolution imaging was achieved with a 78μm×78μm field of view and 12.5 Hz-40 Hz imaging frequency.Mono and dual-color continuous super-resolution images of microtubules and cargo in cells were obtained by ISM.The signal-to-noise ratio of the obtained images was improved from 3.94 to 22.81 and the positioning accuracy of cargoes was enhanced by FCEDN from 15.83±2.79 nm to 2.83±0.83 nm.As a general image enhancement method,FCEDN can be applied to various types of microscopy systems.Application with conventional spinning disk confocal microscopy was demonstrated and significantly improved images were obtained.
作者
唐云青
周才微
高慧文
孙育杰
Yun-Qing Tang;Cai-Wei Zhou;Hui-Wen Hao;Yu-Jie Sun(Institute of Systems Biomedicine,School of Basic Medical Sciences,Peking University Health Science Center,Beijing 100191,China;Academy for Advanced Interdisciplinary Studies,Peking University,Beijing 100871,China;State Key Laboratory of Membrane Biology,Biomedical Pioneer Innovation Center(BIOPIC,Beijing 100871,China;School of Life Sciences,Peking University,Beijing 100871,China;School of Future Technology,Peking University,Beijing 100871,China)
基金
Project supported by the China Postdoctoral Science Foundation,the National Key Research and Development Program of China for Y.S.(Grant No.2017YFA0505300)
the National Science Foundation of China for Y.S.(Grant No.21825401)。
