A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 29, 2018

Abstract

BACKGROUND AND OBJECTIVE: Prostate segmentation on Magnetic Resonance (MR) imaging is problematic because disease changes the shape and boundaries of the gland and it can be difficult to separate the prostate from surrounding tissues. We propose an automated model that extracts and combines multi-level features in a deep neural network to segment prostate on MR images.

Authors

  • Ke Yan
    Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wis.
  • Xiuying Wang
    Otolaryngology Department, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Jinman Kim
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: jinman.kim@sydney.edu.au.
  • Mohamed Khadra
    Nepean Urology Research Group, Nepean Hospital, Penrith New South Wales, Australia.
  • Michael Fulham
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Australia. Electronic address: michael.fulham@sydney.edu.au.
  • Dagan Feng
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiao Tong University, China. Electronic address: dagan.feng@sydney.edu.au.

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