title: Fully automatic and nonparametric quantification of adipose tissue in fat-water separation MR imaging.
abstract: Despite increasing demand and research efforts, currently there is no consensus on the protocol for automated and reliable quantification of adipose tissue (AT) and visceral adipose tissue (VAT) using MRI. The purpose of this study was to propose a novel computational method with enhanced objectiveness for the quantification of AT and VAT in fat-water separation MRI. 3T data from IDEAL were acquired for the fat-water separation. Fat tissues were separated from nonfat regions (background air, bone, water, and other nonfat tissues) using K-means clustering (K = 2). From the binary fat mask, arm regions were separated from body based on the relative size of connected component. AT was obtained from the binary body fat mask. With the initial contour as the outer boundary of body fat, the subcutaneous adipose tissue (SAT) and VAT were separated using deformable model driven by a specifically generated deformation field pointing to the inner boundary of SAT. The proposed method was tested on 16 patients with dyslipidemia and evaluated by comparing the correlation with semi-automatic segmentation results. Good robustness was also observed in the proposed method from the Bland-Altman plots. Compared to other established fat segmentation methods, the proposed method is highly objective for fat-water separation MRI with minimal variability induced by subjective parameter settings.
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.|Research Center for Medical Image Computing, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.|CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, People's Republic of China.
authors.forename: Defeng
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China. shilin@cuhk.edu.hk.|Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China. shilin@cuhk.edu.hk.
authors.forename: Lin
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China. winniechu@cuhk.edu.hk.
authors.forename: Winnie C W
authors.initials: WC
authors.lastname: Chu
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.
authors.forename: Miao
authors.initials: M
authors.lastname: Hu
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.
authors.forename: Brian
authors.initials: B
authors.lastname: Tomlinson
authors.affiliation: Institute of Clinical Anatomy, Southern Medical University, Guangzhou, People's Republic of China.
authors.forename: Wen-Hua
authors.initials: WH
authors.lastname: Huang
authors.affiliation: Shenzhen Key Laboratory of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, People's Republic of China.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.
authors.forename: Pheng Ann
authors.initials: PA
authors.lastname: Heng
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.
authors.forename: David K W
authors.initials: DK
authors.lastname: Yeung
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People's Republic of China.
authors.forename: Anil T
authors.initials: AT
authors.lastname: Ahuja
mesh_terms: D050153:Abdominal Fat / Q000502:physiology*; D000328:Adult; D050171:Dyslipidemias; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D050152:Intra-Abdominal Fat / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D050151:Subcutaneous Fat / Q000502:physiology*; D014867:Water / Q000737:chemistry*
chemical_list: D014867:Water
keywords: Abdominal fat; Automated segmentation; Subcutaneous adipose tissue; Visceral adipose tissue