  BACKGROUND AND PURPOSE: Cerebral aneurysm is one of the most common
cerebrovascular diseases, and SAH caused by its rupture has a very high
mortality and disability rate. Existing automatic segmentation methods based on
DLMs with TOF-MRA modality could not segment edge voxels very well, so that our
goal is to realize more accurate segmentation of cerebral aneurysms in 3D
TOF-MRA with the help of DLMs. MATERIALS AND METHODS: In this research, we
proposed an automatic segmentation framework of cerebral aneurysm in 3D
TOF-MRA. The framework was composed of two segmentation networks ranging from
coarse to fine. The coarse segmentation network, namely DeepMedic, completed
the coarse segmentation of cerebral aneurysms, and the processed results were
fed into the fine segmentation network, namely dual-channel SE_3D U-Net trained
with weighted loss function, for fine segmentation. Images from ADAM2020
(n=113) were used for training and validation and images from another center
(n=45) were used for testing. The segmentation metrics we used include DSC, HD,
and VS. RESULTS: The trained cerebral aneurysm segmentation model achieved DSC
of 0.75, HD of 1.52, and VS of 0.91 on validation cohort. On the totally
independent test cohort, our method achieved the highest DSC of 0.12, the
lowest HD of 11.61, and the highest VS of 0.16 in comparison with
state-of-the-art segmentation networks. CONCLUSIONS: The coarse-to-fine
framework, which composed of DeepMedic and dual-channel SE_3D U-Net can segment
cerebral aneurysms in 3D TOF-MRA with a superior accuracy.
