ArticleBiophysics reports2021
Advancing biological super-resolution microscopy through deep learning: a brief review.
Article in Biophysics reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- O-SNAP uncovers nanoscale chromatin remodeling in dedifferentiation and stress responses.Nature communications · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.ACS nano · 2026Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Advances in multimodal imaging techniques in nanomedicine: enhancing drug delivery precision.RSC advances · 2025Review
- Dissection of Neurochemical Pathways Across Complexity and Scale.Journal of neurochemistry · 2025Review
- Lab-on-a-chip device for microfluidic trapping and TIRF imaging of single cells.Biomedical microdevices · 2025Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation.Light, science & applications · 2023Article
- Live-cell super-resolution imaging unconventional dynamics and assemblies of nuclear pore complexes.Biophysics reports · 2023Article
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
- Development of Deep-Learning-Based Single-Molecule Localization Image Analysis.International journal of molecular sciences · 2022Review
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Authors and funding
4 authors.
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Abstract
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 constraints 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 discussed. 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 research area.
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