Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

Journal: Medical physics
Published Date: May 29, 2023

Abstract

PURPOSE: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineation task.

Authors

  • Miao Tian
    Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
  • Hongqiu Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Xingang Liu
    State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute, Sichuan University, Chengdu, 610065, China.
  • Yuyun Ye
    Department of Electrical and Computer Engineering, University of Tulsa, Tulsa, USA.
  • Ganlu Ouyang
    Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China.
  • Yali Shen
    Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China.
  • Zhiping Li
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Shaozhi Wu
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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