Purpose: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.
Materials and Methods: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and singleslice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.
Results: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2±0.6% for SAT, and 2.7±2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.
Conclusion: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.
- Abdominal fat
- Image processing
- Intra-abdominal fat
- Subcutaneous fat
- Visceral adipose tissue
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging