Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Nov 12, 2022

Abstract

BACKGROUND AND PURPOSE: Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strategy using the patient's previous images and contours to update the model and improve segmentation accuracy and efficiency for MRgART.

Authors

  • Zhenjiang Li
    Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China; Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Baosheng Li
    Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, 250117, China.
  • Jian Zhu
  • Yinglin Peng
    Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
  • Chengze Li
    Manteia Technologies Co.,Ltd, 1903, B Tower, Zijin Plaza, No.1811 Huandao East Road, Xiamen, 361001, China. Electronic address: Lcz13629716324@163.com.
  • Jennifer Zhu
    Department of biochemistry and molecular biology, University of British Columbia, Canada, 8 Edenstone View NW, Calgary AB, Canada T3A 3Z2. Electronic address: jynniferazhu@gmail.com.
  • Qichao Zhou
    Manteia Technologies Co., Ltd, Xiamen, P. R. China.
  • Yong Yin
    Department of Radiation Oncology, Shandong Cancer Hospital, Jinan, Shandong, 250117, China.

Keywords

External Resources

Popular Topics
Recent Journals