3d Volume Analysis
3d Volume Analysis - Related Videos
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 10 •
2017
Segmentation of three-dimensional data from many imaging techniques is a major bottleneck in analysis of complex biological systems. Here, we describe the use of SuRVoS Workbench to semi-automatically segment volumetric data at various length-scales using example datasets from cryo-electron tomography, cryo soft X-ray tomography, and phase contrast X-ray tomography techniques.
08:15
Cardiac Pressure-Volume Loop Analysis Using Conductance Catheters in Mice
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Cited by 33 •
2015
Cardiac pressure-volume loop analysis is the most comprehensive way to measure cardiac function in the intact heart. We describe a technique to perform and analyze cardiac pressure volume loops, using conductance catheters.
04:22
Mouse Inguinal Lymph Node Volume Analysis: An Ultra-high-frequency Ultrasound Imaging Technique to Determine the Volume of Inguinal Lymph Node in Mouse Model
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2025
In this video, we demonstrate the technique to image the inguinal lymph node in a mouse model using ultra-high-frequency ultrasound imaging. This non-invasive imaging technique allows the monitoring of the change in lymph node volume in the animal over time.
12:45
Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
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Cited by 10 •
2022
Tunneling nanotubes (TNTs) are primarily open-ended F-actin membrane nanotubes that connect neighboring cells, facilitating intercellular communication. The notable characteristic that distinguishes TNTs from other cell protrusions is the hovering nature of the nanotubes between cells. Here, we characterize TNTs by constructing a 3D volume view of confocal z-stack images.
11:29
Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
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Cited by 15 •
2014
A new computational system featuring GPU-accelerated molecular dynamics simulation and 3D/VR visualization, analysis and manipulation of nanostructures has been implemented, representing a novel approach to advance materials research and promote innovative investigation and alternative methods to learn about material structures with dimensions invisible to the human eye.
FAQs
Segmentation is the key separation step: it identifies the structures or regions that should be measured within a three-dimensional dataset. Once relevant regions are distinguished, the analysis can calculate their individual volume, surface area, dimensions, or distribution rather than treating the entire dataset as one object. This supports targeted engineering inspection and characterization.
The source data determines what the analysis can represent. Imaging data can describe an existing component or material, while computer-aided design data provides a model for examining intended geometry. Using either source as the basis for volumetric modeling allows engineers to evaluate form, internal geometry, or component characteristics in the context most relevant to inspection or design.
The analysis can produce several complementary measurements, including volume, surface area, dimensions, and spatial distribution. Volume quantifies the amount of three-dimensional space occupied, while surface area and dimensions describe geometric characteristics. Distribution and spatial relationships add information about where regions occur relative to one another, supporting more detailed interpretation of parts and materials.
A typical workflow begins with measured or reconstructed data, which is converted into a volumetric model. Segmentation then separates the relevant structures or regions from the rest of the dataset. Finally, geometric and spatial properties are calculated for the selected regions. This sequence transforms three-dimensional representation into quantitative information that can support engineering evaluation.
Engineering applications include inspection of manufactured parts, characterization of materials, analysis of porous structures, and evaluation of components represented by imaging or computer-aided design data. The same general approach can therefore address both manufactured geometry and material structure, giving engineers quantitative information for examining parts, internal features, and three-dimensional organization.
By revealing internal geometry and detecting changes or defects, the method provides information that may not be available from surface examination alone. Engineers can use the resulting measurements to improve inspection and quality control, assess component or material characteristics, and inform modeling and design decisions. Its value comes from connecting three-dimensional structure with measurable engineering properties.