ArticleNature communications2022
Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Fast Submillimeter Whole-Brain TMagnetic resonance in medicine · 2026Article
- Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.Science advances · 2026Article
- Fast and accessible morphology-free functional fluorescence imaging analysis.PLoS computational biology · 2026Article
- From Controlled Scenarios to the Real World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration.Research (Washington, D.C.) · 2026Article
- Imaging Diagnosis of Mild Cognitive Impairment: A Bibliometric Analysis (1999-2024).Brain and behavior · 2025Article
- Integrative functional optical imaging approaches: Optics, microfluidics, and machine learning for neuroscience in organoids and small-animal models.Biophysical journal · 2025Review
- Article
- High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising.Nature communications · 2025Article
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Deep-prior ODEs augment fluorescence imaging with chemical sensors.Nature communications · 2024Article
- Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis.Nature communications · 2024Article
- Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.Scientific reports · 2024Article
- Simultaneous removal of noise and correction of motion warping in neuron calcium imaging using a pipeline structure of self-supervised deep learning models.Biomedical optics express · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Microfluidic Localized Hydrogel Polymerization Enables Simultaneous Recording of Neural Activity and Behavior inReaction chemistry & engineering · 2024Article
- Compact simultaneous label-free autofluorescence multi-harmonic microscopy for user-friendly photodamage-monitored imaging.Journal of biomedical optics · 2024Article
- The fusion of microfluidics and artificial intelligence: a novel alliance for medical advancements.Bioanalysis · 2024Article
- Video-rate 3D imaging of living cells using Fourier view-channel-depth light field microscopy.Communications biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50-70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors.
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.