Automatic detection algorithm for intracranial aneurysm based on 3D-RA image(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第8期
- Page:
- 950-956
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Automatic detection algorithm for intracranial aneurysm based on 3D-RA image
- Author(s):
- ZHANG Jianhua1; LIU Xinke2; ZHAO Yan1; YANG Xu1
- 1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China 2. Neurointerventional Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China 3. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
- Keywords:
- Keywords: intracranial aneurysm 3D-RA imagepriori knowledge optical flow convolutional neural network
- PACS:
- R318；TP391.9
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.08.006
- Abstract:
- Abstract: Intracranial aneurysm which is a kind of high incidence cerebrovascular disease seriously threatening human life and health has low detection rate and high mortality after rupture. Aiming at the problem of insufficient utilization of prior knowledge by 2D convolution neural network in aneurysm diagnosis, an algorithm for intracranial aneurysm detection using deformable convolution integrated with optical flow is proposed based on the imaging characteristics of 3D-RA sequence images. Dense optical flow algorithm is used to obtain the optical flow information between sequential images as prior knowledge.Then the obtained optical flow information is used to improve the 2D convolution calculation process, and an optical flow deformable convolution model is proposed to establish the pixel-level connection between sequential images. In addition, an encoding module is composed of optical flow deformable convolution and standard convolution to extract important features from images. The 3D-AR intracranial angiography data from 360 clinical cases in Beijing Tiantan Hospital are taken as sample set, and the test results showed that the accuracy, precision, recall value, F1 score, AUC and mAP of the proposed method are 0.978 7, 0.983 6, 0.974 7, 0.979 1, 0.992 4 and 0.982 2 respectively. The accuracy of the proposed network in detecting intracranial aneurysm is higher than that of traditional networks U-net and Attention U-net. Compared with traditional deformable convolution model, optical flow deformable convolution model uses optical flow as prior knowledge, which improves network performance.
Last Update: 2022-09-05