以 3D U-Net 关注为基础的轻光磁共振动脉动图像中脑动脉动脉动成像自动探测方法 (An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net)

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.

翻译：脑动脉瘤断裂导致的皮肤出血,这往往会导致致命的后果。然而,如果在无症状期发现和处理动脉瘤,破裂的可能性就会大大降低。目前,飞行时空磁共振动血管造影术是最常用的非侵入性筛查技术之一。在脑动脉瘤检测中,应用深度学习技术可以有效改善动脉感应度的筛选效果。进行的研究发现,三维特征在动脉感应检测中起着重要作用,但是它们需要大量的训练数据,而且有问题,比如高假正率。方法:本文提出了一个用于脑动脉冲检测的新型方法。首先,一个完全自动的脑动脉冲分解算法,在没有培训数据的情况下,用于提取文件显示的容量,然后,3D U-Net通过3D 改进了动脉冲感应的检测效果。 2. 进行的研究发现三维特征在动脉冲感应检测模型中具有重要的作用,在完全自动的测试系统中也使用了这种测试方法。