Development of Deep-Learning-Based Single-Molecule Localization Image Analysis |
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| Authors: | Yoonsuk Hyun Doory Kim |
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| Affiliation: | 1.Department of Mathematics, Inha University, Incheon 22212, Korea;2.Department of Chemistry, Hanyang University, Seoul 04763, Korea;3.Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Korea;4.Institute of Nano Science and Technology, Hanyang University, Seoul 04763, Korea;5.Research Institute for Natural Sciences, Hanyang University, Seoul 04763, Korea |
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| Abstract: | Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging. |
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| Keywords: | single-molecule localization microscopy super-resolution microscopy deep learning computer vision |
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