Super Voxels
Super Voxels - Related Videos
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.
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.
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...
06:08
A Novel Method: Super-selective Adrenal Venous Sampling
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Cited by 44 •
2017
'Super-selective' adrenal venous sampling (ssAVS, also called segmental adrenal venous sampling: sAVS) was performed using a micro-catheter to identify adrenal segment(s) that produce excessive amounts of hormones. The ssAVS technique was described, cases in which adrenal segmental lesion(s) were identified by ssAVS were presented, and the usefulness of ssAVS in future adrenal research was discussed.
08:47
Super-resolution Imaging of the Bacterial Division Machinery
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Cited by 17 •
2013
We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.
FAQs
Supervoxels are generated by evaluating neighboring voxels together rather than treating every voxel as an independent element. The algorithm compares properties such as intensity, color, texture, and spatial location while favoring compact local regions. This combined decision helps regions follow meaningful image boundaries, which is important when adjacent biological structures have different visual characteristics.
Each feature provides a different basis for deciding whether nearby voxels belong in the same region. Intensity and color describe visual signal, texture captures local appearance, and spatial properties encourage compactness and neighborhood coherence. Their combined use lets the representation reflect both what a region looks like and where it occurs in the volume.
Grouping voxels into Super Voxels creates a smaller set of meaningful local regions for subsequent processing. This reduction can make complex volumetric datasets more manageable and improve the efficiency of computational workflows. Because the regions retain relevant spatial and image properties, researchers can simplify analysis without discarding the organization needed to study biological structures.
An analysis begins with a three-dimensional image, where an algorithm groups adjacent voxels according to their visual and spatial properties. The resulting regions can support segmentation and quantitative analysis, followed by feature extraction, classification, or visualization. This workflow replaces an unwieldy collection of individual voxels with a smaller set of locally meaningful units for examining biological structure or change.
Applications span microscopy, medical imaging, and volumetric tissue data. Within these datasets, the regions can aid identification of cells, organelles, and anatomical structures. The same representation therefore supports analyses at different biological scales, from cellular components to larger tissue or anatomical organization, while retaining a three-dimensional image context.
Supervoxels provide a region-based foundation for feature extraction, classification, and visualization, while also making quantitative analysis more efficient. In biological studies, this organization can help researchers characterize structure and assess change in volumetric data. Its value extends beyond segmentation because the same local regions can support multiple computational analyses of complex biological images.