ArticleNature biotechnology2018
Deep learning massively accelerates super-resolution localization microscopy.
Article in Nature biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 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
212 citing papers in PubMed.
- Biphasic SMN condensates safeguard stress-induced cytoplasmic mislocalization of splicing RNPs.Cellular and molecular life sciences : CMLS · 2026Article
- Quantifying the spatio-temporal image degradation under motion blur in fluorescence microscopy.Biophysical reports · 2026Article
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.Light, science & applications · 2026Article
- From EM to AI: Multiscale imaging of the secretory pathway with insights from plant cells.Journal of microscopy · 2026Review
- Combined TIRF and 3D Super-Resolution Microscopy for Nanoscopic Characterization of Adhesion Molecules on Microvilli.Analytical chemistry · 2026Article
- Diffusing caveolin-1 scaffolds regulate mechanosignalling.Nature cell biology · 2026Article
- SIMBA: an agentic AI platform for single-molecule multidimensional imaging.bioRxiv : the preprint server for biology · 2026Article
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- Metal-Enhanced Fluorescence for the Biologist's Cellular Imaging Toolkit: Design Principles and Recent Applications.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Self-Supervised Isotropic Resolution Enhancement of Expansion Microscopy via Quantized Compression.Journal of imaging informatics in medicine · 2026Article
- Super-resolution functional photoacoustic microscopy via label-free cell tracking.Light, science & applications · 2026Article
- Functional super-resolution microscopy of fibers and polymers: convergence of artificial and biological systems at the nanoscale.Nanoscale horizons · 2026Review
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- Single-Molecule Localization Expansion Nanoscopy via Spontaneously Blinking Fluorophores.Chemical & biomedical imaging · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- Deep Learning Super-Resolution Spectrometer Based on Fiber Random Laser With Ultrahigh Spectral Purity.Nanophotonics (Berlin, Germany) · 2026Article
- LensPlus: a high space-bandwidth optical imaging technique.Biomedical optics express · 2026Article
- Artificial Neural Networks for Bioimage Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Deep learning-based morphological analysis of human sperm.Medical & biological engineering & computing · 2026Article
152 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.
Indexed as
Identifiers
29658943What 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.