Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning.

Journal: Magnetic resonance imaging
Published Date: Dec 1, 2019

Abstract

Accurate measuring of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) is vital for the research of many diseases. The localization and quantification of SAT and VAT by computed tomography (CT) expose patients to harmful ionizing radiation. Magnetic resonance imaging (MRI) is a safe and painless test. The aim of this paper is to explore a practical method for the segmentation of SAT and VAT based on the iterative decomposition of water and fat with echo asymmetry and least square estimation‑iron quantification (IDEAL-IQ) technology and machine learning. The approach involves two main steps. First, a deep network is designed to segment the inner and outer boundaries of SAT in fat images and the peritoneal cavity contour in water images. Second, after mapping the peritoneal cavity contour onto the fat images, the assumption-free K-means++ with a Markov chain Monte Carlo (AFK-MC) clustering method is used to obtain the VAT content. An MRI data set from 75 subjects is utilized to construct and evaluate the new strategy. The Dice coefficients for the SAT and VAT content obtained from the proposed method and the manual measurements performed by experts are 0.96 and 0.97, respectively. The experimental results indicate that the proposed method and the manual measurements exhibit high reliability.

Authors

  • Ning Shen
    State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, 130012 Changchun, China. Electronic address: shenning17@mails.jlu.edu.cn.
  • Xueyan Li
    College of Electronic Science and Engineering, Jilin University, Changchun, China. Electronic address: leexy@jlu.edu.cn.
  • Shuang Zheng
    Shanghai Advanced Research Institute Chinese Academy of Sciences: Chinese Academy of Sciences Shanghai Advanced Research Institute, Advanced Separation & Conversion on Engineered Nanopore Dynamics Laboratory, CHINA.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yu Fu
    Molecular Diagnosis and Treatment Center for Infectious Diseases Dermatology Hospital Southern Medical University Guangzhou China.
  • Xiaoming Liu
    College of Agriculture, Northeast Agricultural University, Harbin, China.
  • Mingyang Li
    Department of Industrial and Management Systems Engineering, The University of South Florida, Tampa, FL, United States.
  • Jiasheng Li
    State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, 130012 Changchun, China. Electronic address: jali17@mails.jlu.edu.cn.
  • Shuxu Guo
    College of Electronic Science and Engineering, Jilin University, Changchun, China.
  • Huimao Zhang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.). Electronic address: huimao@jlu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals