哈尔滨工程大学学报2024，Vol.45Issue(4)：786-793,8.DOI:10.11990/jheu.202206003
脑血管数字减影血管造影高分辨率分割网络设计
Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels
摘要
Abstract
To solve the problem of low accuracy of existing convolutional neural networks for cerebral vascular DSA image segmentation,an improved network based on U-Net(IC-Net)is proposed.By fusing the use of inception and channel attention modules,rich vascular feature information is extracted using multiple sensory domains and feature information is filtered.A new 7×7 convolutional layer is added to reduce the amount of data generated dur-ing training by compressing the feature layer resolution.Compared with the U-Net and common U-Net improved models,the improved model's intersection over union,accuracy,F1-score,and area under the curve increase by 1.82%,0.014%,1.19%,and 0.73%on average,respectively.The results verify that the IC-Net model remark-ably improves the model's capabilities to detect weak vessels and vessel ends in cerebrovascular digital subtraction angiography images and distinguish artifactual noise.The model provides a strong reference for physicians to identi-fy lesions within cerebrovascular vessels.关键词
图像分割/特征提取/脑血管/数字减影血管造影/U-Net/Inception模块/通道注意力/降维处理Key words
image segmentation/feature extraction/cerebrovascular/digital subtraction angiography/U-Net/in-ception module/channel attention/dimension reduction treatment分类
信息技术与安全科学引用本文复制引用
崔颖,付瑞,朱佳,高山,陈立伟,张广..脑血管数字减影血管造影高分辨率分割网络设计[J].哈尔滨工程大学学报,2024,45(4):786-793,8.基金项目
国家自然科学基金项目(81901190). （81901190）
