JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (9): 13-20.doi: 10.6040/j.issn.1671-9352.0.2020.655

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ASR-UNet: an improved retinal vessels segmentation algorithm based on attention mechanism

San-li YI1,2(),Jian-ting CHEN1,Jian-feng HE1,*()   

  1. 1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
    2. Key Laboratory of Computer Technology Application of Yunnan Province, Kunming 650500, Yunnan, China
  • Received:2020-11-24 Online:2021-09-20 Published:2021-09-13
  • Contact: Jian-feng HE E-mail:152514845@qq.com;120112624@qq.com

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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

CLC Number: 

  • TP391

Fig.1

SE module structure"

Fig.2

SE-Resnet module structure"

Fig.3

Attention gate module"

Fig.4

Improved retinal vessel segmentation algorithm model of ASR-UNet"

Fig.5

Retinal image channel contrast"

Fig.6

Patches"

Table 1

Evaluation index and formula"

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}} $

Fig.7

Segmentation on DRIVE test dataset"

Table 2

Performance metrics for different algorithms in DRIVE database"

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

Table 3

Performance metrics for different algorithms in CHASE_DB1 database"

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

Table 4

Performance metrics for different algorithms in DRIVE test database"

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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