Auto-segmentation of important centers of growth in the pediatric skeleton to consider during radiation therapy based on deep learning.

Journal: Medical physics
PMID: 36047281

Abstract

BACKGROUND: Routinely delineating of important skeletal growth centers is imperative to mitigate radiation-induced growth abnormalities for pediatric cancer patients treated with radiotherapy. However, it is hindered by several practical problems, including difficult identification, time consumption, and inter-practitioner variability.

Authors

  • Wenlong Qiu
    Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P. R. China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Xingmin Ma
    System Second Department, North China Institute of Computing Technology, Beijing 100083, China.
  • Youyong Kong
    Lab of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. kongyouyong@seu.edu.cn.
  • Pengyue Shi
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P. R. China.
  • Min Fu
    Mathematics Department, School of Information, Renmin University of China, Beijing, China.
  • Dandan Wang
    Department of Traditional Chinese Medicine Orthopedics and Traumatology, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Man Hu
    Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China.
  • Xianjun Zhou
    Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, P. R. China.
  • Qian Dong
    Celgene Corporation, Summit, NJ, USA.
  • Qichao Zhou
    Manteia Technologies Co., Ltd, Xiamen, P. R. China.
  • Jian Zhu

Keywords

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