Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2933-2940.doi: 10.13229/j.cnki.jdxbgxb20220550

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Classification and recognition of retinal vessels based on attention U⁃Net

Yang YAN1(),Zi-ru YOU1,Yuan YAO2,Wen-bo HUANG1()   

  1. 1.College of Computer Science and Technology,Changchun Normal University,Changchun 130032,China
    2.Bureau of Major Tasks,Chinese Academy of Sciences,Beijing 100864,China
  • Received:2022-05-09 Online:2022-12-01 Published:2022-12-08
  • Contact: Wen-bo HUANG E-mail:7685746@qq.com;huangwenbo@sina.com

Abstract:

Aiming at the limitations of automatic classification method for retinal artery and vein vessels(A/V), an automatic retinal A/V classification method based on attention U-Net (Attention U-Net,AU-Net)was proposed. The retinal A/V feature information was enhanced by using vascular structure information, topological relationship and edge information. The attention block was introduced into the VC-Net network model, which improvemented on U-Net, combining the local and global information, adjusting weight to restrict the retinal A/V features, such as inhibiting the background tendency features and enhancing the vascular edge and end features, so as to realize the accurate classification of retinal A/V. The method was tested in the DRIVE data set. The retinal A/V classification accuracy is 0.9685, F1 value is 0.9886, sensitivity is 0.9803 and specificity is 0.9957. The experimental results show that compared with the classical U-Net, the performance indexes of the proposed method are significantly improved, which can be used for clinical reference.

Key words: deep learning, classification of artery and vein vessels, attention block, U-Net

CLC Number: 

  • TP391.7

Fig.1

Network architecture diagram of AU-Net"

Fig.2

Retinal A/V angiography at different scales"

Fig.3

Schematic diagram of ResNet structure[13]and Res2Net structure[14]"

Fig.4

Schematic diagram of convolution and deconvolution operation"

Fig.5

Structure of attention block"

Table 1

Performance evaluation of retinal A/V classification with different loss functions"

dicefocal交叉熵LACC
0.09340.9501
0.07570.9521
0.05890.9534
0.03710.9685

Table 2

Performance evaluation of DRIVE dataset classification"

方法ACCSESP
Ronneberger[250.91220.91450.9083
Morano[260.95540.76460.9836
Girard[270.94800.74900.9770
Chen[280.96290.95280.9714
AU-Net0.96850.98030.9957

Fig.6

Retinal artery and vein vessels segmentation and classification results on the DRIVE dataset"

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