[Automated Pre-delineation of CTV in Patients with Cervical Cancer Using Dense V-Net].

Journal: Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Published Date:

Abstract

We use a dense and fully connected convolutional network with good feature learning in small samples, to automatically pre-deline CTV of cervical cancer patients based on CT images and evaluate the effect. The CT data of stage IB and IIA postoperative cervical cancer with similar delineation scope were selected to be used to evaluate the pre-sketching accuracy from three aspects:sketching similarity, sketching offset and sketching volume difference. It has been proved that the 8 most representative parameters are superior to those with single network and reported internationally before. Dense V-Net can accurately predict CTV pre-delineation of cervical cancer patients, which can be used clinically after simple modification by doctors.

Authors

  • Wen Guo
    School of Physics Science and Technology, Wuhan University, Wuhan, China.
  • Zhongjian Ju
    Department of Radiation Oncology, People's Liberation Army General Hospital, Beijing 100853, P.R.China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Shanshan Gu
    Department of Radiation Oncology, People's Liberation Army General Hospital, Beijing 100853, P.R.China.
  • Jin Zhou
  • Xiaohu Cong
    Radiotherapy Department, First Medical Center, General Hospital of Chinese People's Liberation Army, Beijing, 100853.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Xiangkun Dai
    Department of Radiotherapy, First Medical Center of PLA General Hospital, BeiJing 100853, P.R.China.