Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men

Abstract

To develop an automatic segmentation algorithm to classify abdominal adipose tissues into visceral fat (VAT), deep (DSAT), and superficial (SSAT) subcutaneous fat compartments and evaluate its performance against manual segmentation. Data were acquired from 44 normal (BMI 18.0-22.9 kg/m(2) ) and 38 overweight (BMI 23.0-29.9 kg/m(2) ) subjects at 3T using a two-point Dixon sequence. A fully automatic segmentation algorithm was developed to segment the fat depots. The first part of the segmentation used graph cuts to separate the subcutaneous and visceral adipose tissues and the second step employed a modified level sets approach to classify deep and superficial subcutaneous tissues. The algorithmic results of segmentation were validated against the ground truth generated by manual segmentation. The proposed algorithm showed good performance with Dice similarity indices of VAT/DSAT/SSAT: 0.92/0.82/0.88 against the ground truth. The study of the fat distribution showed that there is a steady increase in the proportion of DSAT and a decrease in the proportion of SSAT with increasing obesity. The presented technique provides an accurate approach for the segmentation and quantification of abdominal fat depots.