Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

Journal: Current medical imaging
PMID: 34455965

Abstract

BACKGROUND: Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem.

Authors

  • Yisong He
    Department of Radiotherapy, West China Hospital of Sichuan University, Chengdu, 610041.
  • Shengyuan Zhang
    Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, PR. China.
  • Yong Luo
    Laboratory Department of the First Affiliated Hospital of Shenzhen University, Shenzhen, 518000, China.
  • Hang Yu
  • Yuchuan Fu
    Department of Radiotherapy, West China Hospital of Sichuan University, Chengdu, 610041.
  • Zhangwen Wu
    Key Laboratory of Radiation Physics and Technology of Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu 610064, Sichuan Province, PR. China.
  • Xiaoxuan Jiang
    Department of Radiotherapy, West China Hospital of Sichuan University, Chengdu, 610041.
  • Ping Li
    Department of Gastroenterology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.

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