Chinese Journal of Magnetic Resonance ›› 2022, Vol. 39 ›› Issue (3): 267-277.doi: 10.11938/cjmr20223004

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Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning

Meng CHEN1,Chen GENG2,Yu-xin LI3,Dao-ying GENG3,Yi-fang BAO3,*(),Ya-kang DAI1,2,*()   

  1. 1. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221000, China
    2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215000, China
    3. Department of Radiology, Huashan Hospital, Fudan University, Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200000, China
  • Received:2022-05-24 Online:2022-09-05 Published:2022-07-20
  • Contact: Yi-fang BAO,Ya-kang DAI E-mail:bao_yifang@163.com;daiyk@sibet.ac.cn

Abstract:

Subarachnoid hemorrhage caused by the rupture of cerebral aneurysms is extremely fatal and disabling. It’s imperative for radiologists to achieve efficient screening with the help of deep learning-based models. To improve the detection sensitivity of time of flight-magnetic resonance angiography (TOF-MRA) images, this study proposed a neural network named DCAU-Net which is based on fuzzy labels, 3D U-Net variant, and dual-branch channel attention (DCA), and able to adaptively adjust the response of channel features to improve feature extraction capability. First, TOF-MRA images from 260 subjects were preprocessed, and the data were split into the training set (N=174), validation set (N=43) and testing set (N=43). Then the preprocessed data were used for training and validating DCAU-Net. The results show that DCAU-Net scores 90.69% of sensitivity, 0.83 per case of false positive count and 0.52 of positive predicted value in the testing set, providing a promising tool for detecting cerebral aneurysms.

Key words: cerebral aneurysm, automatic detection, deep learning, fuzzy label, dual branch channel attention mechanism

CLC Number: