Retinal Morphometry
Retinal Morphometry - Related Videos
12:48
In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
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Cited by 28 •
2015
Microglia activation and microgliosis are key responses to chronic neurodegeneration. Here, we present methods for in vivo, long-term visualization of retinal CX3CR1-GFP+ microglial cells by confocal ophthalmoscopy, and for threshold and morphometric analyses to identify and quantify their activation. We monitor microglial changes during early stages of age-related glaucoma.
09:17
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Experience shapes the brain. It is well understood that our brains are different as a result of learning. While many experience-related changes manifest themselves at the microscopic level, for example by neurochemical adjustments in the behavior of individual neurons, we may also examine anatomical changes to the structure of the brain at a macroscopic level. One famous example of this kind of change...
03:48
Preparation of Retinal Samples for Volume Electron Microscopy
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2024
This protocol describes the detailed steps for preparing retinal samples for volume electron microscopy, focusing on the structural features of retinal photoreceptor terminals.
05:43
Preparing Retinal Organoid Samples for Transmission Electron Microscopy
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Cited by 1 •
2024
This protocol provides an optimized and elaborate preparation procedure for retinal organoid samples for transmission electron microscopy. It is suitable for applications that involve the analysis of synapses in mature retinal organoids.
05:15
Investigating Retinal Circuits and Molecular Localization by Pre-Embedding Immunoelectron Microscopy
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2024
This protocol outlines the detailed steps of pre-embedding immunoelectron microscopy, with a focus on exploring synaptic circuits and protein localization in the retina.
FAQs
Layer thickness, surface area, volume, and optic nerve features capture different aspects of retinal organization. Thickness can describe individual layers, while area and volume characterize broader structural extent; optic nerve measurements add information about a major retinal neural feature. Considering these measures together helps researchers relate anatomy to neuronal development, aging, injury, and disease rather than relying on one structural value.
Segmentation is a central analytical step because it separates retinal layers within an image before measurements are calculated. Once boundaries are identified, researchers can extract thickness, area, or volume values for defined structures and compare them consistently. Reliable segmentation therefore connects the visual image to quantitative data and supports analyses of regional differences or changes across time points.
Standardization makes measurements comparable across individuals, retinal regions, and repeated observations. Using consistent measurement definitions and analysis conditions reduces ambiguity about what a reported thickness, area, or volume represents. This comparability allows researchers to distinguish meaningful anatomical variation from differences caused by how structures were measured, strengthening the use of retinal values as structural biomarkers.
The retina contains neural tissue whose organization can change during development, aging, injury, and disease. Quantitative retinal measurements therefore provide a way to study structural consequences of these processes in a neural system. This perspective supports investigations of glaucoma and neurodegenerative disorders, while also connecting retinal anatomy with broader questions about neural damage and organization.
A basic workflow begins by acquiring retinal images with optical coherence tomography or fundus photography. Researchers then segment the relevant retinal layers or optic nerve features, extract standardized measurements such as thickness, area, or volume, and compare the resulting values across people, regions, or time points. The sequence converts imaging data into quantitative structural evidence for neuroscience studies.
It is useful when a study needs objective structural information about retinal changes associated with glaucoma, neurodegenerative disorders, diabetes-related damage, or other conditions. Measurements can characterize how anatomy differs among individuals or regions and can help examine the effects of injury or disease. In this context, morphometry supports research questions by providing quantified tissue-level outcomes.
Measurements collected at multiple time points can show whether retinal structure remains stable or changes during a study. Because the values are standardized, researchers can examine progression and assess treatment effects using objective structural outcomes. This longitudinal use is especially relevant when investigating conditions such as glaucoma, neurodegenerative disorders, or diabetes-related damage, where anatomical change is an important research endpoint.