title: Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The presented technique provides an accurate approach for the segmentation and quantification of abdominal fat depots.
authors.affiliation: Singapore Institute for Clinical Sciences, Agency for Science, Technology & Research (A*STAR), Singapore; Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore.
authors.forename: Suresh Anand
authors.initials: SA
authors.lastname: Sadananthan
authors.forename: Bhanu
authors.initials: B
authors.lastname: Prakash
authors.forename: Melvin Khee-Shing
authors.initials: MK
authors.lastname: Leow
authors.forename: Chin Meng
authors.initials: CM
authors.lastname: Khoo
authors.forename: Hong
authors.initials: H
authors.lastname: Chou
authors.forename: Kavita
authors.initials: K
authors.lastname: Venkataraman
authors.forename: Eric Y H
authors.initials: EY
authors.lastname: Khoo
authors.forename: Yung Seng
authors.initials: YS
authors.lastname: Lee
authors.forename: Peter D
authors.initials: PD
authors.lastname: Gluckman
authors.forename: E Shyong
authors.initials: ES
authors.lastname: Tai
authors.forename: S Sendhil
authors.initials: SS
authors.lastname: Velan
mesh_terms: D050154:Adiposity; D000328:Adult; D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D050152:Intra-Abdominal Fat / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009765:Obesity / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D050507:Subcutaneous Fat, Abdominal / Q000473:pathology*; D013382:Subtraction Technique; D055815:Young Adult
keywords: deep subcutaneous fat; graph cut; level sets; superficial subcutaneous fat; visceral fat