ArticleBiomedical optics express2021
Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
What it found
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Who cites it
26 citing papers in PubMed, 53 citations in OpenAlex.
- An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy.Methods and applications in fluorescence · 2025Article
- Spiner, deep learning-based automated detection of spiral ganglion neurons in intact cochleae.iScience · 2025Article
- A wavelet-guided transformer approach for autofocus in brightfield biological microscopy.Scientific reports · 2025Article
- Active remote focus stabilization in oblique plane microscopy.Biomedical optics express · 2025Article
- GJFocuser: a Gaussian difference and joint learning-based autofocus method for whole slide imaging.Biomedical optics express · 2025Article
- Active Remote Focus Stabilization in Oblique Plane Microscopy.bioRxiv : the preprint server for biology · 2024Article
- Posterior approach to correct for focal plane offsets in lattice light-sheet structured illumination microscopy.Journal of biomedical optics · 2024Article
- DeepFocus: fast focus and astigmatism correction for electron microscopy.Nature communications · 2024Article
- Intelligent Beam Optimization for Light-Sheet Fluorescence Microscopy through Deep Learning.Intelligent computing (Washington, D.C.) · 2024Article
- Surgical procedure of intratympanic injection and inner ear pharmacokinetics simulation in domestic pigs.Frontiers in pharmacology · 2024Article
- Enhancing Light-Sheet Fluorescence Microscopy Illumination Beams through Deep Design Optimization.bioRxiv : the preprint server for biology · 2023Article
- Deep learning-based adaptive optics for light sheet fluorescence microscopy.Biomedical optics express · 2023Article
- COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains.Cell reports methods · 2023Article
- Bulk and mosaic deletions ofiScience · 2023Article
- Maternal organophosphate flame retardant exposure alters the developing mesencephalic dopamine system in fetal rat.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Focus prediction of medical microscopic images based on Lightweight Densely Connected with Squeeze-and-Excitation Network.Frontiers in neuroscience · 2023Article
- Structural and functional imaging of brains.Science China. Chemistry · 2023Review
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
- Ontogeny of cellular organization and LGR5 expression in porcine cochlea revealed using tissue clearing and 3D imaging.iScience · 2022Article
- Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy.Biomedical optics express · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network's prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution.
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.