ArticleNature biotechnology2023
Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed, 205 citations in OpenAlex.
- SIMworks-an integrated software suite for quality-controlled augmented quantitative structured illumination microscopy.Nature protocols · 2026Review
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Advances, Challenges, and Opportunities in Electrochemiluminescence Bioimaging.Chemical & biomedical imaging · 2026Review
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- 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
- High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution.Nature communications · 2026Article
- Super-resolution microscopy: revolutionizing life sciences through advanced resolution.Biophysics reports · 2026Article
- Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.Chemical & biomedical imaging · 2026Review
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured illumination microscopy.Nature methods · 2026Article
- High-speed blind structured illumination microscopy via unsupervised algorithm unrolling.Nature communications · 2026Article
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- Single-Molecule and Super-Resolution Techniques for Investigating Protein Organizations and Biological Functions of Immune Cells.Advances in experimental medicine and biology · 2026Review
- Inductive bias influences the spatial scale of biological features learned from images.Bioinformatics advances · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Deep learning-driven conversion of scanning superlens microscopy to high depth-of-field SEM-like imaging.Microsystems & nanoengineering · 2025Article
- When imaging technology meets single-cell omics: new paradigm in developmental biology.Cell regeneration (London, England) · 2025Review
- A bright and regenerative array tag with fluorogenic ligands for long-term live-cell nanoscopy and single-molecule tracking.Nature communications · 2025Article
- Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM.Nature communications · 2025Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.
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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.