Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

Journal: European radiology
PMID: 33409788

Abstract

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT.

Authors

  • Xianjing Zhao
    Shanghai Institute of Medical Imaging, Shanghai, China.
  • Kaixing Chen
    Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China.
  • Ge Wu
    Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China.
  • Guyue Zhang
    Ping An Technology (Shenzhen) Co., Ltd., Shanghai, China.
  • Xin Zhou
    School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
  • Chuanfeng Lv
    Ping An Healthcare Technology, Shang Hai, PR China.
  • Shiman Wu
    Department of Radiology, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Jing'an District, Shanghai, 200040, China.
  • Yun Chen
  • Guotong Xie
    Ping An Health Technology, Beijing, China.
  • Zhenwei Yao
    Shanghai Institute of Medical Imaging, Shanghai, China. zwyao@fudan.edu.cn.

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