ArticleBiomedical optics express2022
Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Spiner, deep learning-based automated detection of spiral ganglion neurons in intact cochleae.iScience · 2025Article
- Optical sectioning methods in three-dimensional bioimaging.Light, science & applications · 2025Review
- Model based optimization for refractive index mismatched light sheet imaging.Optics express · 2024Article
- Roadmap on computational methods in optical imaging and holography [invited].Applied physics. B, Lasers and optics · 2024Review
- 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
- Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.
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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.