Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI.

Authors

  • Yu-Chun Lin
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Gigin Lin
    Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
  • Sumit Pandey
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan.
  • Chih-Hua Yeh
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Jiun-Jie Wang
    Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Chien-Yu Lin
    Department of Radiation Oncology, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Tsung-Ying Ho
    Departments of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Sheung-Fat Ko
    Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan.
  • Shu-Hang Ng
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.