Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.

Journal: Oral oncology
PMID: 36446186

Abstract

OBJECTIVE: We examined a modified encoder-decoder architecture-based fully convolutional neural network, OrganNet, for simultaneous auto-segmentation of 24 organs at risk (OARs) in the head and neck, followed by validation tests and evaluation of clinical application.

Authors

  • Yinglin Peng
    Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
  • Yimei Liu
    a State Key Laboratory of Oncology in South China , Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center , Guangzhou , China.
  • Guanzhu Shen
    Department of Radiation Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Zijie Chen
    Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China.
  • Meining Chen
    Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Jingjing Miao
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Chong Zhao
    State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, 510060, P. R. China.
  • Jincheng Deng
    Shenying Medical Technology (Shenzhen) Co., Ltd., Shenzhen, Guangdong, China.
  • Zhenyu Qi
    Institute of Automation, Chinese Academy of Sciences (CAS), China. Electronic address: zhenyu.qi@ia.ac.cn.
  • Xiaowu Deng
    Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China.

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