  Background:Subarachnoid hemorrhage caused by ruptured cerebral aneurysm often
leads to fatal consequences.However,if the aneurysm can be found and treated
during asymptomatic periods,the probability of rupture can be greatly
reduced.At present,time-of-flight magnetic resonance angiography is one of the
most commonly used non-invasive screening techniques for cerebral aneurysm,and
the application of deep learning technology in aneurysm detection can
effectively improve the screening effect of aneurysm.Existing studies have
found that three-dimensional features play an important role in aneurysm
detection,but they require a large amount of training data and have problems
such as a high false positive rate. Methods:This paper proposed a novel method
for aneurysm detection.First,a fully automatic cerebral artery segmentation
algorithm without training data was used to extract the volume of interest,and
then the 3D U-Net was improved by the 3D SENet module to establish an aneurysm
detection model.Eventually a set of fully automated,end-to-end aneurysm
detection methods have been formed. Results:A total of 231 magnetic resonance
angiography image data were used in this study,among which 132 were training
sets,34 were internal test sets and 65 were external test sets.The presented
method obtained 97.89% sensitivity in the five-fold cross-validation and
obtained 91.0% sensitivity with 2.48 false positives/case in the detection of
the external test sets. Conclusions:Compared with the results of our previous
studies and other studies,the method in this paper achieves a very competitive
sensitivity with less training data and maintains a low false positive rate.As
the only method currently using 3D U-Net for aneurysm detection,it proves the
feasibility and superior performance of this network in aneurysm detection,and
also explores the potential of the channel attention mechanism in this task.
