Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels

被引：0

作者：

Cui Y. ^{[1
]}

Fu R. ^{[1
]}

Zhu J. ^{[1
]}

Gao S. ^{[1
]}

Chen L. ^{[1
]}

Zhang G. ^{[2
]}

机构：

[1] School of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2024年 / 45卷 / 04期

关键词：

cerebrovascular; channel attention; digital subtraction angiography; dimension reduction treatment; feature extraction; image segmentation; inception module; U-Net;

D O I：

10.11990/jheu.202206003

中图分类号：

学科分类号：

摘要：

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 during 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 remarkably 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 identify lesions within cerebrovascular vessels. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.

引用

页码：786 / 793

页数：7

共 18 条

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