ArticleNature communications2025
Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Physics-paired stimulated Raman scattering microscopy enables label-free phenotyping of lipid droplets 3D motility in live cells.Light, science & applications · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Using rotational integration of oblique interferometric scattering to track axial spatiotemporal responses of tubular membrane protrusions.Nature communications · 2026Article
- An end-to-end hybrid deep-learning approach for single-shot wavefront sensing and correction.Nature communications · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- High-fidelity intravital imaging of biological dynamics with latent-space-enhanced digital adaptive optics.Nature biotechnology · 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
- Review
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Phase-based computational adaptive optics enables artifact-free super-resolution microscopy.Communications engineering · 2026Article
- NeuroDecon: A Neural Network-Based Method for Three-Dimensional Deconvolution of Fluorescent Microscopic Images.International journal of molecular sciences · 2025Article
- A Real-Time Cell Image Segmentation Method Based on Multi-Scale Feature Fusion.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics.JACS Au · 2025Review
- Deep structural brain imaging via computational three-photon microscopy.Journal of biomedical optics · 2025Article
- Cellpose3: one-click image restoration for improved cellular segmentation.Nature methods · 2025Article
- System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring.Nature communications · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
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26 authors.
Funding
Abstract
Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.
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