Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks
Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks
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DOI：
10.1186/s12938-019-0726-2
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发表时间：
2019-11-14
影响因子：
3.9
通讯作者：
Zhou, Liangxue
中科院分区：
文献类型：
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作者：
Duan, Haihan;Huang, Yunzhi;Zhou, Liangxue
背景颅内动脉瘤是一种脑血管疾病，可导致多种疾病。临床上，诊断颅内动脉瘤采用数字减影血管造影（DSA）模式作为金标准。现有的自动计算机辅助诊断（CAD）的研究与DSA模式的基础上，经典的数字图像处理（DIP）的方法。然而，经典的特征提取方法受到复杂的血管分布的严重阻碍，并且滑动窗口方法在搜索和特征提取过程中非常耗时。因此，开发一种准确、高效的CAD方法来检测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，我们提出了一个两阶段的卷积神经网络（CNN）架构，自动检测颅内动脉瘤的2D-DSA图像。在区域定位阶段（RLS），我们的检测系统可以定位特定的区域，以减少其他区域的干扰。然后，在动脉瘤检测阶段（ADS），检测器可以联合收割机正面和侧面血管造影视图的信息来识别颅内动脉瘤，并采用假阳性抑制算法。结果本实验在颈内动脉（伊卡）后交通动脉（PCoA）区进行。数据集包含241名受试者用于模型训练，40名前瞻性收集的受试者用于测试。与经典的DIP方法相比，其准确率为62.5%，曲线下面积（AUC）为0.69，所提出的架构可以实现93.5%的准确率和AUC为0.942。此外，我们的方法的检测时间成本约为0.569 s，这是一个百倍于经典的DIP方法的62.546 s。结论与现有的经典DIP方法相比，本文提出的基于CNN的两阶段结构具有更高的精度和更快的速度。总之，我们的研究证明了使用CNN辅助医生在DSA图像上检测颅内动脉瘤是可行的。
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.