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Link to original content: https://doi.org/10.1007/s11517-015-1347-y
Fully automatic and nonparametric quantification of adipose tissue in fat–water separation MR imaging | Medical & Biological Engineering & Computing Skip to main content
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Fully automatic and nonparametric quantification of adipose tissue in fat–water separation MR imaging

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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.

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Acknowledgments

The work described in this paper was supported by Grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Nos. CUHK 416712, 14113214, 473012, and SEG_CUHK02), Knowledge Transfer Fund at CUHK (Project ID: TBF14MED009), a grant from Lui Che Woo Foundation, and a grant from Shenzhen Science and Technology Innovation Committee (Project No. CXZZ20140606164105361).

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Authors and Affiliations

  1. Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People’s Republic of China

    Defeng Wang, Winnie C. W. Chu, David K. W. Yeung & Anil T. Ahuja

  2. Research Center for Medical Image Computing, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People’s Republic of China

    Defeng Wang

  3. CUHK Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, People’s Republic of China

    Defeng Wang

  4. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People’s Republic of China

    Lin Shi, Miao Hu & Brian Tomlinson

  5. Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People’s Republic of China

    Lin Shi

  6. Institute of Clinical Anatomy, Southern Medical University, Guangzhou, People’s Republic of China

    Wen-Hua Huang

  7. Shenzhen Key Laboratory of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, People’s Republic of China

    Tianfu Wang

  8. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong, People’s Republic of China

    Pheng Ann Heng

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  1. Defeng Wang
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  2. Lin Shi
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  3. Winnie C. W. Chu
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  4. Miao Hu
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  7. Tianfu Wang
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  9. David K. W. Yeung
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  10. Anil T. Ahuja
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Corresponding authors

Correspondence to Lin Shi or Winnie C. W. Chu.

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Wang, D., Shi, L., Chu, W.C.W. et al. Fully automatic and nonparametric quantification of adipose tissue in fat–water separation MR imaging. Med Biol Eng Comput 53, 1247–1254 (2015). https://doi.org/10.1007/s11517-015-1347-y

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  • DOI: https://doi.org/10.1007/s11517-015-1347-y

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