As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly used methods for screening aneurysms. The computer-assisted detection system for cerebral aneurysms can help clinicians improve the accuracy of aneurysm diagnosis. As fully convolutional network could classify the image pixel-wise, its three-dimensional implementation is highly suitable for the classification of the vascular structure. However, because the volume of blood vessels in the image is relatively small, 3D convolutional neural network does not work well for blood vessels. The presented study developed a computer-assisted detection system for cerebral aneurysms in the contrast-unenhanced time-of-flight magnetic resonance angiography image. The system first extracts the volume of interest with a fully automatic vessel segmentation algorithm, then uses 3D-UNet-based fully convolutional network to detect the aneurysm areas. A total of 131 magnetic resonance angiography image data are used in this study, among which 76 are training sets, 20 are internal test sets and 35 are external test sets. The presented system obtained 94.4% sensitivity in the fivefold cross-validation of the internal test sets and obtained 82.9% sensitivity with 0.86 false positive/case in the detection of the external test sets. The proposed computer-assisted detection system can automatically detect the suspected aneurysm areas in contrast-unenhanced time-of-flight magnetic resonance angiography images. It can be used for aneurysm screening in the daily physical examination.

中文翻译：

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飞行时间磁共振血管造影中使用全卷积网络的自动计算机辅助脑动脉瘤检测系统。

由于脑动脉瘤破裂可能导致致命的结果，因此尽早发现未破裂的动脉瘤可以挽救生命。目前，造影剂未增强的飞行时间磁共振血管造影是最常用的筛查动脉瘤的方法之一。脑动脉瘤的计算机辅助检测系统可以帮助临床医生提高动脉瘤诊断的准确性。由于全卷积网络可以按像素对图像进行分类，因此其三维实现非常适合对血管结构进行分类。但是，由于图像中血管的数量相对较小，因此3D卷积神经网络不适用于血管。提出的研究开发了一种计算机辅助的脑动脉瘤检测系统，该系统在造影剂未增强的飞行时间磁共振血管造影图像中进行检测。该系统首先使用全自动血管分割算法提取感兴趣的体积，然后使用基于3D-UNet的全卷积网络来检测动脉瘤区域。本研究共使用了131个磁共振血管造影图像数据，其中76个是训练集，20个是内部测试集，35个是外部测试集。所提出的系统在内部测试集的五重交叉验证中获得了94.4％的灵敏度，在外部测试集的检测中获得了82.9％的灵敏度（0.86假阳性/例）。所提出的计算机辅助检测系统可以在造影剂未增强的飞行时间磁共振血管造影图像中自动检测可疑的动脉瘤区域。可在日常体检中用于动脉瘤筛查。