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

Ying CUI; Rui FU; Jia ZHU; Shan GAO; Liwei CHEN; Guang ZHANG

doi:10.11990/jheu.202206003

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

更新时间：2025-09-01

- Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels
- Journal of Harbin Engineering University Vol. 45, Issue 4, Pages: 786-793(2024)
- 作者机构：
  
  1.哈尔滨工程大学信息与通信工程学院, 黑龙江哈尔滨 150001
  2.哈尔滨医科大学附属第一医院神经外科, 黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.11990/jheu.202206003
  CLC： TP391
- Received：02 June 2022，
  
  Online First：01 February 2024，
  
  Published：05 April 2024
- 稿件说明：
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Ying CUI, Rui FU, Jia ZHU, et al. Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels[J]. Journal of Harbin Engineering University, 2024, 45(4): 786-793.
DOI：

Ying CUI, Rui FU, Jia ZHU, et al. Design of a high-resolution segmentation network for digital subtraction angiography of cerebral vessels[J]. Journal of Harbin Engineering University, 2024, 45(4): 786-793. DOI： 10.11990/jheu.202206003.

摘要

针对现存卷积神经网络对脑血管数字减影血管造影分割精度不高的问题

本文提出了一种基于U-Net的改进网络(IC-Net)。通过融合使用Inception和CAM通道注意力模块

以多种感受域提取更丰富的血管特征信息

并对特征信息进行筛选。增加7×7卷积层

通过压缩特征层分辨率的方式减少训练过程中产生的数据量。本文所提模型与U-Net、R2U-Net、Attention U-Net相比

IOU、Accuracy、F1-Score和ROC曲线下面积4项指标平均提升了1.82 %、0.014 %、1.19 %和0.73 %。结果验证了IC-Net模型明显提升了脑血管数字减影血管造影虚弱血管和血管末端的检测能力

提升了分辨伪影噪声的能力

为医生识别脑血管中产生的病变提供有力参考。

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 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.

关键词

Keywords

references

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