BACKGROUND An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. METHODS In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. RESULTS Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. CONCLUSION The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.

中文翻译：

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使用级联卷积神经网络从数字减影血管造影术中自动检测颅内动脉瘤。

背景技术颅内动脉瘤是可导致多种疾病的脑血管疾病。临床上，颅内动脉瘤的诊断利用数字减影血管造影（DSA）模式作为金标准。现有的具有DSA模式的自动计算机辅助诊断（CAD）研究是基于经典数字图像处理（DIP）方法的。然而，经典的特征提取方法由于复杂的血管分布而受到严重阻碍，并且滑动窗口方法在搜索和特征提取期间非常耗时。因此，开发一种准确有效的CAD方法以检测DSA图像上的颅内动脉瘤是一项有意义的任务。方法在这项研究中 我们提出了一种两阶段卷积神经网络（CNN）架构来自动检测2D-DSA图像上的颅内动脉瘤。在区域定位阶段（RLS），我们的检测系统可以定位特定区域，以减少其他区域的干扰。然后，在动脉瘤检测阶段（ADS），该检测器可以利用假阳性抑制算法，结合前侧血管造影和侧向血管造影的信息，以识别颅内动脉瘤。结果我们的研究是在颈内动脉（ICA）的后交通动脉（PCoA）区域进行的。数据集包含241个模型训练对象和40个预期收集的对象以进行测试。与经典DIP方法的精度为62.5％，曲线下面积（AUC）为0.69相比，所提出的架构可以达到93.5％的精度和0.942的AUC。此外，我们的方法的检测时间成本约为0.569 s，比传统的DIP方法62.546 s快一百倍。结论结果表明，与现有的经典DIP方法研究相比，我们提出的基于CNN的两阶段体系结构更加准确，更快。总体而言，我们的研究表明，协助医生使用CNN在DSA图像上检测颅内动脉瘤是可行的。结论结果表明，与现有的经典DIP方法研究相比，我们提出的基于CNN的两阶段体系结构更加准确，更快。总体而言，我们的研究表明，协助医生使用CNN在DSA图像上检测颅内动脉瘤是可行的。结论结果表明，与现有的经典DIP方法研究相比，我们提出的基于CNN的两阶段体系结构更加准确，更快。总体而言，我们的研究表明，协助医生使用CNN在DSA图像上检测颅内动脉瘤是可行的。