首页|Advancing biological super-resolution microscopy through deep learning:a brief review

Advancing biological super-resolution microscopy through deep learning:a brief review

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Biological super-resolution microscopy is a new generation of imaging techniques that overcome the～200 nm diffraction limit of conventional light microscopy in spatial resolution.By providing novel spatial or spatiotemporal information on biological processes at nanometer resolution with molecular specificity,it plays an increasingly important role in biomedical sciences.However,its technical con-straints also require trade-offs to balance its spatial resolution,temporal resolution,and light exposure of samples.Recently,deep learning has achieved breakthrough performance in many image processing and computer vision tasks.It has also shown great promise in pushing the performance envelope of biological super-resolution microscopy.In this brief review,we survey recent advances in using deep learning to enhance the performance of biological super-resolution microscopy,focusing primarily on computational reconstruction of super-resolution images.Related key technical challenges are dis-cussed.Despite the challenges,deep learning is expected to play an important role in the development of biological super-resolution microscopy.We conclude with an outlook into the future of this new re-search area.

Super-resolution microscopyImage super-resolutionDeep learningImage reconstructionFluores-cence microscopy

Tianjie Yang、Yaoru Luo、Wei Ji、Ge Yang

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Institute of Biophysics,Chinese Academy of Sciences,Beijing 100101,China

College of Life Sciences,University of Chinese Academy of Sciences,Beijing 100049,China

Laboratory of Computational Biology and Machine Intelligence,School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100190,China

National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

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XDB37040402XDB370401049195420131971289292019000056115200M001

2021

生物物理学报
中国生物物理学会 中国科学院生物物理研究所

生物物理学报

影响因子：0.355
ISSN：1000-6737
年,卷(期)：2021.7(4)
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