Automated Eye Scanning
Automated Eye Scanning - Related Videos
08:34
Automated Charting of the Visual Space of Housefly Compound Eyes
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Cited by 2 •
2022
The protocol here describes the measurement of the spatial organization of the visual axes of housefly eyes, mapped by an automatic device, using the pseudopupil phenomenon and the pupil mechanism of the photoreceptor cells.
02:28
Leaky Scanning
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2020
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
09:03
Trabecular Meshwork Response to Pressure Elevation in the Living Human Eye
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Cited by 7 •
2015
Trabecular meshwork (TM) migration into Schlemm’s canal space can be induced by acute pressure elevation by ophthalmodynamometer, and observed by spectral domain optical coherence tomography. The goal of this method is to quantify the morphometric response of the living outflow tract to acute pressure elevation in living tissues in situ.
05:10
An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
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Cited by 2 •
2023
In this paper, we present a novel automated method for assessing visual acuity in infants and toddlers by using an eye-tracking system.
09:33
Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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Cited by 2 •
2018
Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.
FAQs
The imaging system first records ocular structures, including the retina, optic nerve, and blood vessels. Computer algorithms then process those images to identify visible features, quantify morphology, and detect patterns that may need review. Separating image capture from computational analysis helps create a consistent workflow and supports comparisons across many scans.
These regions provide complementary biological information. Retinal structure can support studies of visual development and aging, the optic nerve contributes relevant ocular measurements, and blood vessels offer a view of vascular biology. Examining several structures allows automated eye scanning to represent changes across different parts of the eye rather than relying on a single measurement.
Computer-based feature identification and morphology measurements reduce dependence on repeated manual judgments. Applying the same image-processing approach across scans can make assessments more consistent and easier to compare. This reproducibility is especially valuable in quantitative biology, where researchers may examine changes over time or analyze large numbers of ocular images.
A study typically begins by acquiring ocular images with an imaging device, followed by computational processing of the recorded regions. The software identifies features, measures morphology, and flags patterns for review. Researchers can then use the resulting measurements or review indicators in screening workflows, longitudinal comparisons, or quantitative analyses of ocular health.
The method is useful when investigators need to compare ocular measurements across repeated observations. Consistent imaging and algorithmic analysis can help track structural or vascular patterns associated with aging, visual development, or disease-related change. Its reproducible measurements support evaluation of trends over time rather than relying only on isolated visual assessments.
Automated eye scanning can produce quantitative measurements of ocular morphology and identify image patterns that warrant further review. These outputs support research on visual development, aging, vascular biology, and disease-related changes. They can also strengthen screening and ocular-health analysis by making image-based assessment more consistent and scalable across study populations.