A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy.

Journal: Medical physics
Published Date: Oct 28, 2018

Abstract

PURPOSE: The purpose of this study was to expedite the contouring process for MRI-guided adaptive radiotherapy (MR-IGART), a convolutional neural network (CNN) deep-learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images.

Authors

  • Yabo Fu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Thomas R Mazur
    Department of Radiation Oncology, Washington University, St. Louis, MO 63110, USA.
  • Xue Wu
    School of Civil Engineering, Southeast University, Nanjing 210096, China.
  • Shi Liu
    Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
  • Xiao Chang
    Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, St.Louis, MO, 63110, USA.
  • Yonggang Lu
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA. Electronic address: yonggang.lu@wustl.edu.
  • H Harold Li
    Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, St.Louis, MO, 63110, USA.
  • Hyun Kim
    School of Life Sciences and Biotechnology, Institute for Microorganisms, Kyungpook National University, Daegu 41566, Korea.
  • Michael C Roach
    Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, St.Louis, MO, 63110, USA.
  • Lauren Henke
    Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, St.Louis, MO, 63110, USA.
  • Deshan Yang
    Department of Radiation Oncology, Washington University School of Medicine, 4921 Parkview Place, Campus Box 8224, St. Louis, MO, 63110, USA.

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