A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

Journal: Journal of applied clinical medical physics
PMID: 38682540

Abstract

PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ at risk (OAR) in high-dose-rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC).

Authors

  • Xian Xue
    Secondary Standard Dosimetry Laboratory, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention (CDC), Beijing, China.
  • Dazhu Liang
    Digital Health China Technologies Co., LTD, Beijing, China.
  • Kaiyue Wang
    Zhejiang Lab, Hangzhou 311100, China.
  • Jianwei Gao
    Digital Health China Technologies Co., LTD, Beijing, China.
  • Jingjing Ding
    Department of General Practice, Tongren Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
  • Fugen Zhou
  • Juan Xu
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China. xujuanbiocc@ems.hrbmu.edu.cn.
  • Hefeng Liu
    Digital Health China Technologies Co., LTD, Beijing, China.
  • Quanfu Sun
    Secondary Standard Dosimetry Laboratory, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention (CDC), Beijing, China.
  • Ping Jiang
    School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; School of Computer Science and Technology, Hubei PolyTechnic University, Huangshi 435003, China. Electronic address: jiangping20140209@gmail.com.
  • Laiyuan Tao
    Digital Health China Technologies Co., LTD, Beijing, China.
  • Wenzhao Shi
    Digital Health China Technologies Co., LTD, Beijing, China.
  • Jinsheng Cheng
    Secondary Standard Dosimetry Laboratory, National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention (CDC), Beijing, China.

Keywords

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