Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.

Journal: Medical physics
Published Date: Jan 4, 2019

Abstract

PURPOSE: To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual-modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)-computed tomography (CT) images.

Authors

  • Zisha Zhong
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • Yusung Kim
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Kristin Plichta
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Bryan G Allen
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Leixin Zhou
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.
  • John Buatti
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Xiaodong Wu
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA.

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