U-Net based deep learning bladder segmentation in CT urography.

Journal: Medical physics
Published Date: Apr 1, 2019

Abstract

OBJECTIVES: To develop a U-Net-based deep learning approach (U-DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer-assisted bladder cancer detection and treatment response assessment pipeline.

Authors

  • Xiangyuan Ma
    Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Lubomir M Hadjiiski
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Jun Wei
    Guangzhou Perception Vision Medical Technology Inc. Guangzhou 510000 China.
  • Heang-Ping Chan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Kenny H Cha
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Richard H Cohan
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Elaine M Caoili
    Department of Radiology, University of Michigan, Ann Arbor, Michigan.
  • Ravi Samala
    Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Chuan Zhou
    Department of Radiology, The University of Michigan, Ann Arbor, MI, 48109, USA.
  • Yao Lu
    Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo First Hospital, Ningbo, China.

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