Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI.

Journal: Medical physics
PMID: 31356693

Abstract

PURPOSE: Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW-MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW-MRI images is cumbersome and has low reproducibility. Therefore in this paper, by using the convolutional neural networks (CNNs), we develop a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the VW of carotid artery and for automated diagnosis of the carotid atherosclerosis with the black-blood (BB) VW-MRI (i.e., the T1-weighted MRI) in a slice-by-slice manner.

Authors

  • Jiayi Wu
    State Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Institute of Condensed Matter Physics, School of Physics, Center for Quantitative Biology, Peking University, Beijing, China.
  • Jingmin Xin
    Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China.
  • Xiaofeng Yang
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Jie Sun
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
  • Dongxiang Xu
    Department of Radiology, University of Washington, Seattle, WA, USA.
  • Nanning Zheng
    Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China.
  • Chun Yuan
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, USA.

Keywords

External Resources

Popular Topics
Recent Journals