Fat Volume Segmentation
Fat Volume Segmentation - Related Videos
13:09
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
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Cited by 26 •
2012
Fat content analysis is routinely conducted in studies utilizing murine obesity models. Emerging methods in small animal CT imaging and analysis are providing for longitudinal detail rich fat content analysis. Here we detail step by step procedures for performing small animal CT imaging, analysis, and visualization.
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.
09:25
Transplantation Into the Mouse Ovarian Fat Pad
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Cited by 7 •
2016
We describe an ovarian fad pad transplantation assay suitable for studies of normal and transformed epithelia of the female reproductive tract. The mouse fat pad allows transplantation of large tissue fragments, is easily accessible for surgery and imaging, and provides the most favorable native environment for tissues of Müllerian origin.
06:11
An Efficient Method to Obtain Dedifferentiated Fat Cells
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Cited by 4 •
2016
We have modified the conditions for DFAT cell generation and provide herein information regarding the use of an improved growth medium for the production of these cells.
06:18
Fat Body Organ Culture System in Aedes Aegypti, a Vector of Zika Virus
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Cited by 15 •
2017
The fat body is the central metabolic organ in insects. We present a live organ culture system that enables the user to study the responses of isolated fat body tissue to various stimuli.
FAQs
The segmentation process evaluates image intensity to distinguish adipose tissue from muscle, organs, and other tissues. Image-processing steps can then refine the identified regions before calculating their volumes. This approach is useful when tissue boundaries can be represented by recognizable image characteristics, but reliable measurements depend on separating fat consistently across the anatomical image.
Machine-learning models provide an alternative to manually defined intensity-based rules by identifying patterns associated with adipose tissue in medical images. They can support separation of fat from neighboring tissues and help standardize measurements across patients or studies. Their value is especially important when researchers need reproducible estimates for body-composition analysis, disease monitoring, or patient-specific assessment.
Separating subcutaneous and visceral fat preserves information about where adipose tissue is distributed rather than reporting only a total amount. These regional measurements can be examined in relation to cardiometabolic risk and obesity assessment. The distinction also allows clinicians and researchers to compare body-fat patterns between patients, disease states, or treatment time points.
Reliability depends on how consistently the imaging data are processed and how accurately fat is separated from muscle, organs, and other tissues. The selected method, whether intensity-based image processing or a machine-learning model, affects the resulting regions and calculated volumes. Standardized segmentation is therefore important for comparing patients, monitoring change, and conducting body-composition research.
A typical workflow begins with a computed tomography or magnetic resonance image, followed by identification of adipose tissue using intensity thresholds, image-processing methods, or machine-learning models. The resulting regions are separated from nonfat tissues, and their volumes are calculated for relevant anatomical areas. Analysts can then use these measurements to evaluate distribution, risk, or change over time.
Fat-volume measurements can support obesity assessment, evaluation of cardiometabolic risk, treatment planning, and monitoring during interventions. Researchers also use them to study body composition and disease progression. Because the measurements quantify regional tissue volumes, they provide a patient-specific basis for comparing anatomical fat distribution rather than relying only on broader descriptions of body fat.
Applying a consistent segmentation approach to images collected at different stages allows investigators to calculate changes in adipose-tissue volume and distribution. Those results can help monitor responses during interventions and examine disease progression. Standardized measurements also improve comparability across research studies, supporting analyses of patient-specific outcomes and relationships between body composition and health.