• Jingjing Sun, Bugao Xu, and Jeanne Freeland-Graves.
• Department of Biomedical Engineering, University of Texas, Austin, Texas.
• Am. J. Hum. Biol. 2016 Nov 1; 28 (6): 757-766.

ObjectivesTo develop a fully-automated algorithm to process axial magnetic resonance imaging (MRI) slices for quantifying abdominal visceral, subcutaneous and total adipose tissues, i.e., VAT, SAT, and TAT, without human intervention or prior knowledge.Materials And MethodsFat regions in single MRI slice or sequence (20 slices) were identified with image processing techniques including region-growing, inhomogeneity correction, fuzzy c-means clustering, and active contours segmentation. The MR images of 85 subjects (60 males and 25 females), whose body mass index (BMI) values ranged from 19.96 to 40.35 kg/m2 , were analyzed using the fully-automated algorithm-the automatic method developed in the research and the widely used semi-automated software (sliceOmatic® Tomovision, Inc.)-the reference method.ResultsThe proposed automated method showed good performance against the reference method to quantify adipose tissues in both single umbilical slice and MRI sequence. The square of the Pearson correlation coefficients (R2 ) based on the results generated from the two methods for VAT/SAT/TAT were 0.977/0.998/0.997 for single slice data and 0.995/0.999/0.999 for volumetric data. The intra-class correlation of visceral adipose tissue (VAT) between the three operators was 0.939 in the reference method, which was improved to 0.999 in the automatic method. The adipose tissue measurements in the slice at Lumbar 3 vertebra have the highest correlation with the total fat volumes across the entire abdomen.ConclusionThe fully-automated algorithm presented in the paper provides an accurate and reliable assessment of abdominal fat without human intervention. Am. J. Hum. Biol. 28:757-766, 2016. © 2016Wiley Periodicals, Inc.© 2016 Wiley Periodicals, Inc.

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