Voxel Volume Analysis
Voxel 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.
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Cited by 23 •
2014
Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.
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...
09:37
Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data
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Cited by 2 •
2016
A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.
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.
FAQs
Voxel counts alone do not necessarily represent equal amounts of three-dimensional space, because the analysis must account for the spatial dimensions assigned to the image data. Applying those dimensions to selected voxels makes regional estimates more meaningful when measurements are compared across datasets, individuals, or time points.
Segmentation determines which voxels belong to the structure or region being measured. The software therefore depends on separating relevant anatomy from the rest of the image before summing voxel contributions. Differences in the selected region can change the resulting volume, making segmentation a central analytical step when comparing brain regions across subjects or conditions.
Image registration aligns image data so corresponding anatomical locations can be compared across datasets. In a volume-analysis workflow, registration helps place measurements from different individuals, conditions, or time points into a meaningful spatial relationship. Without appropriate alignment, apparent differences may reflect mismatched locations rather than changes in the brain region under study.
A basic workflow begins with image data, identifies the region of interest through segmentation, accounts for voxel spatial dimensions, and sums the selected voxel contributions. Registration may be included when datasets need spatial comparison. The resulting measurements can then be compared across individuals or conditions, or examined at multiple time points to evaluate structural change.
Researchers apply these measurements to brain development, aging, neurological disease, and brain injury. The same approach can also evaluate treatment-related structural changes by comparing regional volumes between relevant groups or measurements over time. Its value lies in converting image-based anatomy into quantitative outcomes that support assessment of how brain structure differs or changes in a defined study context.
Regional volume measurements can reveal anatomical differences that are not captured by a general visual description of an image. Researchers may use them to assess specific brain regions across individuals or conditions and to track structural trajectories. Interpretation remains tied to the analyzed region, the image data, and the processing steps used to identify and align the relevant voxels.