In European radiology ; h5-index 62.0
OBJECTIVES : Time of flight magnetic resonance angiography (TOF-MRA) is the primary non-invasive screening method for cerebral aneurysms. We aimed to develop a computer-aided aneurysm detection method to improve the diagnostic efficiency and accuracy, especially decrease the false positive rate.
METHODS : This is a retrospective multicenter study. The dataset contained 1160 TOF-MRA examinations composed of unruptured aneurysms (n = 1096) and normal controls (n = 166) from six hospitals. A total of 1037 examinations acquired from 2013 to 2019 were used as training set; 123 examinations acquired from 2020 to 2021 were used as external test set. We proposed an equalized augmentation strategy based on aneurysm location and constructed a detection model based on dual channel SE-3D UNet. The model was trained with a 5-fold cross-validation in the training set, then tested on the external test set.
RESULTS : The proposed method achieved 82.46% sensitivity on patient-level, 73.85% sensitivity on lesion-level, and 0.88 false positives per case in the external test set. The performance did not show significant differences in subgroups according to the aneurysm site (except ACA), aneurysm size (except smaller than 3 mm), or MRI scanners. The performance preceded the basic SE-3D UNet by increasing 15.79% patient-level sensitivity and decreasing 4.19 FPs/case.
CONCLUSIONS : The proposed automated aneurysm detection method achieved acceptable sensitivity while controlling fairly low false positives per case. It might provide a useful auxiliary tool of cerebral aneurysms MRA screening.
KEY POINTS : • The need for automated cerebral aneurysms detecting is growing. • The strategy of equalized augmentation based on aneurysm location and dual-channel input could improve the model performance. • The retrospective multi-center study showed that the proposed automated cerebral aneurysms detection using dual-channel SE-3D UNet could achieve acceptable sensitivity while controlling a low false positive rate.
Chen Geng, Yifang Bao, Jiajun Zhang, Dongdong Wang, Zhiyong Zhou, Ruoyu Di, Bin Dai, Sirong Piao, Daoying Geng, Meng Chen, Yakang Dai, Yuxin Li
2023-Feb-01
Computer-assisted diagnosis, Deep learning, Intracranial aneurysm, Magnetic resonance angiography
