Chinese Journal of Magnetic Resonance >
Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning
Received date: 2022-05-24
Online published: 2022-07-20
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.
Meng CHEN , Chen GENG , Yu-xin LI , Dao-ying GENG , Yi-fang BAO , Ya-kang DAI . Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2022 , 39(3) : 267 -277 . DOI: 10.11938/cjmr20223004
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