ArticleNature communications2018
A machine learning approach for online automated optimization of super-resolution optical microscopy.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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Who cites it
24 citing papers in PubMed.
- Smart event-triggered MINFLUX microscopy to catch and follow rare events.Nature communications · 2026Article
- Review
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.Nature communications · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- Development of AI-assisted microscopy frameworks through realistic simulation with pySTED.Nature machine intelligence · 2024Article
- Review
- Characterizing the blood-brain barrier and gut barrier with super-resolution imaging: opportunities and challenges.Neurophotonics · 2023Review
- Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition.Nature machine intelligence · 2023Article
- Unravelling cell migration: defining movement from the cell surface.Cell adhesion & migration · 2022Review
- Event-driven acquisition for content-enriched microscopy.Nature methods · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Can super-resolution microscopy become a standard characterization technique for materials chemistry?Chemical science · 2022Review
- Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models.Neurobiology of disease · 2021Review
- Challenges facing quantitative large-scale optical super-resolution, and some simple solutions.iScience · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.
Identifiers
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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.