Toward an Automated Method of Abdominal Fat Segmentation of MR Images

Washington University School of Medicine9 enrolled

Overview

Subjects will undergo a brief magnetic resonance (MRI) scan. The resulting images will be used to compare two abdominal fat segmentation techniques. The first technique is already validated and in use. The second technique was recently developed and has not been validated. The hypothesis is that the second technique will be the faster and more reliable of the two.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Obesity

Eligibility

Sex: ALLMin age: 18 YearsMax age: 70 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * ambulatory * cognitively sound Exclusion Criteria: * body mass index less than 18 or greater than 45 kilograms per square meter

Locations (1)

Washington University School of Medicine

St Louis, Missouri, United States

Outcomes

Primary Outcomes

Visceral Fat Volume With Automated Analysis

This is the measurement of Abdominal Visceral Fat in cubic centimeters as determined with a new automated segmentation program.

Time frame: five minutes

Visceral Fat Volume With Manual Segmentation

This is the measure of visceral fat found with our older manual segmentation method

Time frame: five minutes

Secondary Outcomes

Subcutaneous Fat Volume With Automated Analysis

This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with new automated anatomical segmentation software.

Time frame: five minutes

Subcutaneous Fat Volume With Manual Segmentation

This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with the older manual segmentation technique.

Time frame: five minutes

Data from ClinicalTrials.gov

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