Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:

Abstract

BACKGROUND AND PURPOSE: The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thoroughly. Based on DECT-generated virtual monochromatic images (VMIs), this study proposed a novel deep learning model (MIAU-Net) and evaluated the segmentation performance on the head organs-at-risk (OARs).

Authors

  • Lekang Chen
    School of Physics, Beihang University, Beijing City, 100191, PR China.
  • Shutong Yu
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Xiang Wei
    School of Software Engineering Beijing Jiaotong University Beijing, China. Electronic address: xiangwei@bjtu.edu.cn.
  • Junqian Yang
    School of Physics, Beihang University, Beijing 102206, China.
  • Chong Guo
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
  • Wenjie Zeng
    Epidemiology, University of Florida College of Public Health and Health Professions and College of Medicine, Gainesville, FL, USA.
  • Chao Yang
    Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai 202155, P. R. China.
  • Jueye Zhang
    Peking University School of Physics, Beijing 100871, China.
  • Tian Li
    College of Plant Protection, Southwest University, Chongqing, China.
  • Chen Lin
    Faculty of Business and Economics, University of Hong Kong, Hong Kong SAR 999077, China.
  • Xiaoyun Le
    School of Physics, Beihang University, Beijing 102206, China. Electronic address: xyle@buaa.edu.cn.
  • Yibao Zhang
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital, Beijing, China.