Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

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.

Automated summary

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have led to nanoscale imaging, greatly enhancing our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is limited by the image analysis method. This review discusses the use of deep learning-based algorithms to improve SMLM image analysis and addresses future applications of deep learning methods for SMLM imaging.