ArticleNature methods2024
Self-inspired learning for denoising live-cell super-resolution microscopy.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Physics-informed self-supervised denoising for fluorescence microscopy via structure and intensity priors.iScience · 2026Article
- The multifaceted significance of phosphoinositides in endocytic trafficking.FEBS letters · 2026Review
- Self-supervised image denoising and restoration method for atomic force microscopy.Nature communications · 2026Article
- Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging.Biomedical signal processing and control · 2026Article
- Physics-paired stimulated Raman scattering microscopy enables label-free phenotyping of lipid droplets 3D motility in live cells.Light, science & applications · 2026Article
- Physics-guided self-supervised learning for high-fidelity reconstruction in live-cell imaging.Npj imaging · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.Science advances · 2026Article
- Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics.Nature communications · 2026Article
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- Article
- Diffusion prior-based zero-shot denoising for real-world fluorescence microscopy images via encoder-decoder guided sampling.Biomedical optics express · 2026Article
- Quantum-edge synergy for dynamic acoustic compensation in ultrasound safety: a narrative review of tissue heterogeneity challenges.Frontiers in medical technology · 2026Review
- Leveraging spatial-angular redundancy for self-supervised denoising of 3D fluorescence imaging without temporal dependency.Nature communications · 2025Article
- Real-time self-supervised denoising for high-speed fluorescence neural imaging.Nature communications · 2025Article
- Compressive Fourier-Domain Intensity Coupling (C-FOCUS) enables near-millimeter deep imaging in the intact mouse brain in vivo.Research square · 2025Article
- Clinical translation potential of self-inspired live-cell super-resolution microscopy.Clinical and translational medicine · 2025Article
- Dark-based optical sectioning assists background removal in fluorescence microscopy.Nature methods · 2025Article
- Multiscale and recursive unmixing of spatiotemporal rhythms for live-cell and intravital cardiac microscopy.Nature cardiovascular research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.
Indexed as
Identifiers
39261639What 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.