Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.

Journal: BMC medical imaging
Published Date: Nov 13, 2021

Abstract

BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images.

Authors

  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Zhaonan Sun
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Chao Han
    School of Software Engineering, South China University of Technology, Guangzhou, P. R. China.
  • Yingpu Cui
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Jiahao Huang
    Beijing Smart Tree Medical Technology Co. Ltd., No.24, Huangsi Street, Xicheng District, Beijing, 100011, China.
  • Xiangpeng Wang
    Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang

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