MFA-Net: retinal vessel segmentation based on multi-view context attention

JI Ling-yu; GAO Yong-bin; WEI Zi-ran

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MFA-Net: retinal vessel segmentation based on multi-view context attention

更新时间：2026-09-04

- MFA-Net: retinal vessel segmentation based on multi-view context attention
- Vol. 38, Issue 3, Pages: 14-20(2021)
- 作者机构：
  
  1. 上海工程技术大学机构电子电气工程学院,上海,201620
  2. 上海长征医院军医普通外科,上海,200003
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
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纪玲玉, 高永彬, 卫子然. MFA-Net: 基于多视野上下文关注的视网膜血管分割[J]. 微电子学与计算机, 2021, 38(3): 14-20.

JI Ling-yu, GAO Yong-bin, WEI Zi-ran. MFA-Net: retinal vessel segmentation based on multi-view context attention[J]. 2021, 38(3): 14-20.
纪玲玉, 高永彬, 卫子然. MFA-Net: 基于多视野上下文关注的视网膜血管分割[J]. 微电子学与计算机, 2021, 38(3): 14-20. DOI：

JI Ling-yu, GAO Yong-bin, WEI Zi-ran. MFA-Net: retinal vessel segmentation based on multi-view context attention[J]. 2021, 38(3): 14-20. DOI：

摘要

视网膜血管图像分割在疾病的自动诊断中起着重要的作用，是早期诊断和手术规划的关键步骤.所以视网膜血管树的精确分割己成为计算机辅助诊断的先决条件.随着卷积神经网络在医学图像分割中的应用，一些分割性能优越的网络逐渐被提出.但是他们忽略了上下文多视野的关注，导致微血管分支很难分出.为了解决此问题，本文提出了一种多视野上下文关注的网络架构.该网络融入一个新的多视野关注模块，该模块能够在扩大感受野的同时关注有效信息，防止微血管分割断裂.其次网络融入注意力门控机制，将低级特征和高级特征相结合，产生更具代表性的新特征，进一步提升微血管分割的性能.在公开的两个眼底数据集DRIVE和CHASE DB1进行了评估，实验结果显示在准确率，灵敏度，交并比和AUC(ROC曲线下的面积)上均优于目前的新算法.

Abstract

Retinal vascular image segmentation plays an important role in the automatic diagnosis of diseases

and is a key step in early diagnosis and surgical planning. Thus

accurate segmentation of the retinal vascular tree has become a prerequisite for computer-aided diagnosis. With the application of convolutional neural networks in medical image segmentation

some Network with optimized segmentation performance have been gradually proposed. However

these methods ignore the attention of context and multi-view

making it difficult to separate microvascular branches. To solve this problem

a multi-view context attention network architecture is proposed. Firstly

the network adds a new multi-view attention module

which can effectively focus on the valid information while expanding the receptive field

to prevent the fracture of microvascular segmentation. Secondly

the network integrates attention gate

it combines low-level features with high-level features

produced more representative new features

and further improve the performance of microvascular segmentation. It is evaluated on two public fundus datasets DRIVE and CHASE DB1

the experimental results show that the algorithm is superior to the state-of-the-art methods in accuracy

sensitivity

intersection-over-union and AUC (area under ROC curve).

关键词

Keywords

references

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