ArticleNature biotechnology2026
A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- SIMworks-an integrated software suite for quality-controlled augmented quantitative structured illumination microscopy.Nature protocols · 2026Review
- Organelle-specific fluorescent probes for live imaging at the nanoscale.Science China. Life sciences · 2026Review
- O-SNAP uncovers nanoscale chromatin remodeling in dedifferentiation and stress responses.Nature communications · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Article
- Super-Resolution Structured Illumination Microscopy to Study Endocytosis and Cellular Nanoparticle Uptake.ACS nano · 2026Review
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- Perspective: An outlook on fluorescence tracking.The Journal of chemical physics · 2025Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Unveiling the multifaceted roles of extracellular vesicles in cancer: insights from molecular imaging and engineering strategies.Acta biochimica et biophysica Sinica · 2025Review
- Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network. DPA-TISR adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. We also develop Bayesian DPA-TISR and design an expected calibration error minimization framework that reliably infers inference confidence. We demonstrate multicolor live-cell SR imaging for more than 10,000 time points of various biological specimens with high fidelity, temporal consistency and accurate confidence quantification.
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.