ASR-UNet: 一种基于注意力机制改进的视网膜血管

doi:10.6040/j.issn.1671-9352.0.2020.655

摘要/Abstract

摘要：

针对现有算法因视网膜图像中血管细小和光照等因素导致的分割精度低的问题, 在U-Net的基础上进行改进, 提出了一种能够较好地提取血管结构的算法模型ASR-UNet。首先, 在编码和解码阶段使用了SE-Resnet结构, 引入通道注意力机制对血管细微结构进行通道增强, 之后在跳跃连接部分使用了AG模块对血管细微结构进行空间增强, 提高网络模型对血管细微结构的分割能力。在公开数据集DRIVE和CHASE_DB1上验证了本文的算法, 在评价指标Acc上分别为0.9697和0.9657, 在敏感性上分别为0.8044和0.7673, 在特异性指标上为0.9859和0.9866。实验结果表明, 近年来的视网膜血管分割算法相比, 本文提出的算法在性能有更好的表现。

关键词: 图像分割, U-Net, 视网膜血管分割

Abstract:

Factors such as tiny blood vessels in the retina and light will cause the problem of low segmentation accuracy of existing algorithms. This paper proposes a new retinal vessel segmentation algorithm model based on U-Net segmentation model combined with attention mechanism. The improved measures of this paper are as follows: first, we use SE-Resnet to replace the common convolution used in U-Net, thereby introducing a channel attention mechanism to enhance the original features. After that, AG are added to the jump connection part to enhance the spatial characteristics, thereby improving the models ability to segment the microstructure of blood vessels. The algorithm of this paper is verified on the public datasets DRIVE and CHASE_DB1, and the results are 0.9697 and 0.9657 on Acc, 0.8044 and 0.7673 on sensitivity, and 0.9859 and 0.9866 on specificity. Experimental results show that the proposed algorithm has better performance than recent retinal blood vessel segmentation algorithms.

Key words: image segmentation, U-Net, retinal vessel segmentation

中图分类号:

TP391

易三莉,陈建亭,贺建峰. ASR-UNet: 一种基于注意力机制改进的视网膜血管[J]. 《山东大学学报(理学版)》, 2021, 56(9): 13-20.

San-li YI,Jian-ting CHEN,Jian-feng HE. ASR-UNet: an improved retinal vessels segmentation algorithm based on attention mechanism[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2021, 56(9): 13-20.

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Evaluation metric	Description
Accuracy(Acc)	$ \frac{{TP + TN}}{{TP + TN + FP + FN}} $
Sensitivity(Sn)	$ \frac{{TP}}{{TP + FN}} $
Specificity(Sp)	$ \frac{{TN}}{{TN + FP}} $
F₁-score(F₁)	$ \frac{{2 \times TP}}{{2 \times TP + FP + FN}} $

方法出处	年份	Acc	Sn	Sp	F₁
Vega^[21]	2015	0.9412	0.7444	0.9612	0.6884
Liskowski^[22]	2016	0.9535	0.7811	0.9807	N/A
Wu^[14]	2018	0.9567	0.7844	0.9847	N/A
Alom^[23]	2019	0.9556	0.7792	0.9813	0.8171
Zhun Fan^[15]	2019	0.9661	0.7957	0.9827	0.8033
Our method	2020	0.9697	0.8044	0.9859	0.8256

方法出处	年份	Acc	Sn	Sp	F₁
Roychowdhury^[24]	2015	0.9467	0.7615	0.9575	N/A
Liskowski^[22]	2016	0.9628	0.7816	0.9836	N/A
Li^[25]	2016	0.9527	0.7569	0.9816	N/A
Wu^[14]	2018	0.9631	0.7538	0.9847	N/A
Alom^[23]	2019	0.9634	0.7756	0.9820	0.7928
Our method	2020	0.9657	0.7673	0.9866	0.8105

Model	Acc	Sn	Sp	F₁
U-Net	0.9688	0.7915	0.9861	0.8186
U-Net+AGs	0.9689	0.7911	0.9862	0.8189
U-Net+SE-Resnet	0.9695	0.7758	0.9883	0.8197
Our method	0.9697	0.8044	0.9859	0.8256

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