Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PMID: 40174514

Abstract

PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remains under-validated. We reported an investigation that validated a robust semi-supervised conditional nnU-Net (SSC-nnUNet) model in multiple organs at risk (OARs) and tumor segmentation in lung cancer brachytherapy, also explored its potential in robot-assisted puncture diagnosis and treatment.

Authors

  • Guobin Zhang
    School of Mechanical Engineering, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, 300350, China.
  • Daguang Zhang
    3 Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
  • Qiang Cao
    Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Shubin Yang
    Business School, University of Westminster, London, NW1 5LS, UK. Electronic address: S.Yang@westminster.ac.uk.
  • Yijun Xiao
    Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China; National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), China.
  • Zhenzhong Liu
    Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, China.

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