3d Volume Analysis
3d Volume Analysis - Related Videos
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.
10:53
Analysis of Astrocyte Territory Volume and Tiling in Thick Free-Floating Tissue Sections
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Cited by 4 •
2022
This protocol describes methods for sectioning, staining, and imaging free-floating tissue sections of the mouse brain, followed by a detailed description of the analysis of astrocyte territory volume and astrocyte territory overlap or tiling.
11:38
Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench
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Cited by 8 •
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.
08:21
Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys
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Cited by 2 •
2021
This protocol demonstrates robotic ultrasound (US) as a practical, cost-effective, and quick alternative to traditional non-invasive image modalities.
03:38
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
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2025
Here, we present a protocol to utilize micro-computed tomography (micro-CT) to quantify ventilated regions in a unilateral bleomycin-induced pulmonary fibrosis model via intratracheal instillation on the lesional side.
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.