Automatic prostate segmentation based on fusion between deep network and variational methods.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2019

Abstract

BACKGROUND: Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI.

Authors

  • Lu Tan
    School of Electrical Engineering, Computing and Mathematical Sciences (Computing Discipline), Curtin University, Bentley, Western Australia, Australia.
  • Antoni Liang
    School of Electrical Engineering, Computing and Mathematical Sciences (Computing Discipline), Curtin University, Bentley, Western Australia, Australia.
  • Ling Li
    College of Communication Engineering, Jilin University, Changchun, Jilin China.
  • Wanquan Liu
    North China University of Technology, School of Electrical Information, Beijing, China.
  • Hanwen Kang
    Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC, Australia.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

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