Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks

• Published in: Biomedical engineering online Vol. 18; no. 1; pp. 110 - 18
• Main Authors: Duan, Haihan, Huang, Yunzhi, Liu, Lunxin, Dai, Huming, Chen, Liangyin, Zhou, Liangxue
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14.11.2019; BioMed Central; BMC
• Subjects: Algorithms; Aneurysm; Angiography; Angiography, Digital Subtraction; Artificial neural networks; Automation; Cable television broadcasting industry; Cerebral aneurysm; Computer-aided diagnosis; Convolutional neural networks; Detection equipment; Diagnostic imaging; Digital subtraction angiography; Humans; Image processing; Image Processing, Computer-Assisted - methods; Intracranial aneurysm; Intracranial Aneurysm - diagnostic imaging; Neural Networks, Computer; Object detection; Physicians; United States; Taiwan; Digital subtraction angiography; Convolutional neural networks; Intracranial aneurysm; Computer-aided diagnosis; Object detection
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-019-0726-2

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. 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. 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. 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.