1. Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning
- Author
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Meng CHEN, Chen GENG, Yu-xin LI, Dao-ying GENG, Yi-fang BAO, and Ya-kang DAI
- Subjects
cerebral aneurysm ,automatic detection ,deep learning ,fuzzy label ,dual branch channel attention mechanism ,Electricity and magnetism ,QC501-766 - 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.
- Published
- 2022
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