ReviewInternational journal of molecular sciences2022
Development of Deep-Learning-Based Single-Molecule Localization Image Analysis.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 33 citations in OpenAlex.
- Inverted PAINT for Material-Specific Super-Resolution Fluorescence Imaging of Semiconductors.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Cellular optical imaging techniques: a dynamic advancing frontier.Science China. Life sciences · 2025Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- DHPSFU: a Fiji plugin for fast and accurate double helix-PSF 3D single-molecule localisation microscopy.Scientific reports · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Surpassing the Diffraction Limit in Label-Free Optical Microscopy.ACS photonics · 2024Review
- Precision lung cancer screening from CT scans using a VGG16-based convolutional neural network.Frontiers in oncology · 2024Article
- Simultaneous spectral differentiation of multiple fluorophores in super-resolution imaging using a glass phase plate.Optics express · 2023Article
- Investigating Uncertainties in Single-Molecule Localization Microscopy Using Experimentally Informed Monte Carlo Simulation.Nano letters · 2023Article
- Recent development of computational cluster analysis methods for single-molecule localization microscopy images.Computational and structural biotechnology journal · 2023Review
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.