Computer Morphometry
Computer Morphometry - Related Videos
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...
09:23
Shunt Surgery, Right Heart Catheterization, and Vascular Morphometry in a Rat Model for Flow-induced Pulmonary Arterial Hypertension
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Cited by 15 •
2017
This protocol describes a surgical procedure to create a model for flow-induced pulmonary arterial hypertension (PAH) in rats and the procedures to analyze the principle hemodynamic and histological end-points in this model.
11:50
A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
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Cited by 22 •
2022
A standardized pipeline is presented for examining cerebellum grey matter morphometry. The pipeline combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation and voxel-based registration of the cerebellum for volumetric quantification.
08:52
3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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Cited by 34 •
2017
3D ultrasound imaging (3DUS) allows fast and cost-effective morphometry of musculoskeletal tissues. We present a protocol to measure muscle volume and fascicle length using 3DUS.
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 29 •
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.
FAQs
Segmentation determines which anatomical structure or tissue region will be measured in a medical image. The boundaries selected at this stage directly affect the resulting size, shape, and structural measurements. By isolating relevant regions before extracting numerical data, the analysis can focus on specific organs, tissues, or anatomical areas and support consistent comparisons.
Extracted measurements can be organized for comparison across individuals, time points, or clinical groups. This allows investigators to examine anatomical variation, identify differences associated with disease-related abnormalities, and evaluate whether structural features change over time. The numerical format makes visual findings more suitable for systematic analysis in medical research.
It converts visual information into reproducible numerical data rather than relying only on descriptive visual judgments. This does not eliminate the need to identify relevant structures, but it provides measurements that can be compared more consistently across cases. Such standardization strengthens analyses of anatomy, tissue changes, and differences between clinical populations.
A typical workflow begins with processing medical images, followed by identification of the relevant anatomical structures through segmentation. The system then extracts measurements describing size, shape, or other structural features. Researchers can compare these results across people, clinical groups, or repeated time points to investigate anatomical variation, disease-related changes, or treatment effects.
Computer morphometry supports several medical research areas, including diagnostic imaging, neuroscience, pathology, and personalized medicine. Its value differs by application: measurements can characterize anatomical structures, quantify tissue changes, or provide data for comparing patients and groups. These uses extend image assessment beyond visual inspection by supplying structured quantitative evidence.
Measurements collected at different time points can reveal structural changes that may not be captured adequately by a single image assessment. Comparing these results helps researchers examine whether tissues or anatomical regions change during or after treatment. In medicine, this provides a quantitative way to study treatment effects alongside disease-related abnormalities and normal anatomical variation.