ArticleCommunications biology2023
Deep learning enables fast, gentle STED microscopy.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 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
18 citing papers in PubMed.
- Time-lapse RESOLFT nanoscopy empowered by deep learning.Nature communications · 2026Article
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Live-cell STED microscopy enables 50 nm resolution imaging with preserved cell proliferation.Scientific reports · 2026Article
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- Microscopy in mitochondrial research - a comprehensive review.Journal of cell science · 2026Review
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- Quantitative Stimulated Emission Depletion (STED) Microscopy with DNA-Fluorophore Labels.ACS nano · 2026Article
- A flexible framework for automated STED super-resolution microscopy.Scientific reports · 2026Article
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Nanoscale Imaging of Biological Tissues: Techniques, Challenges and Emerging Frontiers.Nanomaterials (Basel, Switzerland) · 2025Review
- The synaptic vesicle cluster as a controller of pre- and postsynaptic structure and function.The Journal of physiology · 2025Review
- An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy.Methods and applications in fluorescence · 2025Article
- Fast and Long-Term Super-Resolution Imaging of Endoplasmic Reticulum Nano-structural Dynamics in Living Cells Using a Neural Network.Small science · 2025Article
- Super-resolution imaging of the neuronal cytoskeleton.Npj imaging · 2024Review
- Neural network-based processing and reconstruction of compromised biophotonic image data.Light, science & applications · 2024Review
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
Corrections and comments
- Erratum issued
- Update of
Authors and funding
7 authors.
Funding
Abstract
STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.
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.