Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network
Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network
复制标题
使用全卷积网络的飞行时间磁共振血管造影脑动脉瘤自动计算机辅助检测系统
DOI：
10.1186/s12938-020-00770-7
复制
发表时间：
2020-05-29
影响因子：
3.9
通讯作者：
Geng, Daoying
中科院分区：
文献类型：
--
作者：
Geng, Chen;Xia, Wei;Geng, Daoying
背景
由于脑动脉瘤破裂可能导致致命后果，早期发现未破裂的动脉瘤可能挽救生命。目前，非对比增强的时间飞跃磁共振血管造影是筛查动脉瘤最常用的方法之一。脑动脉瘤计算机辅助检测系统可帮助临床医生提高动脉瘤诊断的准确性。由于全卷积网络能够对图像像素进行分类，其三维实现非常适合血管结构的分类。然而，由于图像中血管体积相对较小，3D卷积神经网络对血管效果不佳。
结果
本研究开发了一种用于非对比增强的时间飞跃磁共振血管造影图像中脑动脉瘤的计算机辅助检测系统。该系统首先使用全自动血管分割算法提取感兴趣区域，然后使用基于3D - UNet的全卷积网络检测动脉瘤区域。本研究共使用了131例磁共振血管造影图像数据，其中76例为训练集，20例为内部测试集，35例为外部测试集。所提出的系统在内部测试集的五折交叉验证中获得了94.4%的灵敏度，在外部测试集检测中获得了82.9%的灵敏度，每例假阳性率为0.86。
结论
所提出的计算机辅助检测系统能够自动检测非对比增强的时间飞跃磁共振血管造影图像中可疑的动脉瘤区域。它可用于日常体检中的动脉瘤筛查。
BackgroundAs 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.ResultsThe 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.ConclusionsThe 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.