A coarse-to-fine cascade deep learning neural network for segmenting cerebral aneurysms in time-of-flight magnetic resonance angiography.

Journal: Biomedical engineering online
Published Date: Sep 27, 2022

Abstract

BACKGROUND: Accurate segmentation of unruptured cerebral aneurysms (UCAs) is essential to treatment planning and rupture risk assessment. Currently, three-dimensional time-of-flight magnetic resonance angiography (3D TOF-MRA) has been the most commonly used method for screening aneurysms due to its noninvasiveness. The methods based on deep learning technologies can assist radiologists in achieving accurate and reliable analysis of the size and shape of aneurysms, which may be helpful in rupture risk prediction models. However, the existing methods did not accomplish accurate segmentation of cerebral aneurysms in 3D TOF-MRA.

Authors

  • Meng Chen
    Institute of Industrial and Consumer Product Safety, China Academy of Inspection and Quarantine, Beijing, China.
  • Chen Geng
    Academy for Engineering and Technology, Fudan University, Shanghai, China.
  • Dongdong Wang
    Department of Radiology, Huashan Hospital Affiliated to Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China.
  • Zhiyong Zhou
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.
  • Ruoyu Di
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China.
  • Fengmei Li
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
  • Sirong Piao
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200000, China.
  • Jiajun Zhang
  • Yuxin Li
    University of Cincinnati, Department of Chemistry, 312 College Drive, 404 Crosley Tower, Cincinnati, Ohio 45221-0172, United States.
  • Yakang Dai
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China. Electronic address: daiyk@sibet.ac.cn.

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