ArticleBiomedical optics express2021
Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Longitudinal Imaging of the Foveal Cone Mosaic in CNGA3-Associated Achromatopsia.Investigative ophthalmology & visual science · 2024Article
- High refresh rate display for natural monocular viewing in AOSLO psychophysics experiments.Optics express · 2024Article
- Quantifying image quality in AOSLO images of photoreceptors.Biomedical optics express · 2024Article
- Adaptive Optics Retinal Imaging in RDH12-Associated Early Onset Severe Retinal Dystrophy.Investigative ophthalmology & visual science · 2024Article
- Automatic montaging of adaptive optics SLO retinal images based on graph theory.Biomedical optics express · 2024Article
- Minimum intensity projection of embossed quadrant-detection images for improved photoreceptor mosaic visualisation.Frontiers in ophthalmology · 2024Article
- Adaptive Optics Imaging of Inherited Retinal Disease.Cold Spring Harbor perspectives in medicine · 2023Review
- Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical optics express · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
To mitigate the substantial post-processing burden associated with adaptive optics scanning light ophthalmoscopy (AOSLO), we have developed an open-source, automated AOSLO image processing pipeline with both "live" and "full" modes. The live mode provides feedback during acquisition, while the full mode is intended to automatically integrate the copious disparate modules currently used in generating analyzable montages. The mean (±SD) lag between initiation and montage placement for the live pipeline was 54.6 ± 32.7s. The full pipeline reduced overall human operator time by 54.9 ± 28.4%, with no significant difference in resultant cone density metrics. The reduced overhead decreases both the technical burden and operating cost of AOSLO imaging, increasing overall clinical accessibility.
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.