Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Dec 27, 2024

Abstract

BACKGROUND AND PURPOSE: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' workload, challenges persist due to the microscopic spread of tumor cells undetectable in CT imaging, low-intensity contrast between organs, and inter-observer variability. This study aims to develop and validate a multi-task feature fusion network (MTF-Net) that uses distance-based information to enhance CTV segmentation accuracy.

Authors

  • Zhe Wu
    School of Automation, Central South University, Changsha, China.
  • Liming Lu
    Department of Radiotherapy, Tongling People's Hospital, Anhui 244002, China.
  • Cheng Xu
    School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, 2052 Sydney, Australia.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Bin Zeng
    Jiangxi Key Laboratory of Bioprocess Engineering, Jiangxi Science and Technology Normal University, Nanchang, 330013 People's Republic of China.
  • Mujun Liu
    Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China.

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