Retinal Vessel Image Segmentation Based on RAU-net

ZHANG Lijuan; MEI Chang; LI Chaoran; ZHANG Run

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Infrared Technology > 2021 > 43(12): 1222-1227,1233

ZHANG Lijuan, MEI Chang, LI Chaoran, ZHANG Run. Retinal Vessel Image Segmentation Based on RAU-net[J]. Infrared Technology , 2021, 43(12): 1222-1227,1233.

Citation:

ZHANG Lijuan, MEI Chang, LI Chaoran, ZHANG Run. Retinal Vessel Image Segmentation Based on RAU-net[J]. Infrared Technology , 2021, 43(12): 1222-1227,1233.

ZHANG Lijuan, MEI Chang, LI Chaoran, ZHANG Run. Retinal Vessel Image Segmentation Based on RAU-net[J]. Infrared Technology , 2021, 43(12): 1222-1227,1233.

Citation:

ZHANG Lijuan, MEI Chang, LI Chaoran, ZHANG Run. Retinal Vessel Image Segmentation Based on RAU-net[J]. Infrared Technology , 2021, 43(12): 1222-1227,1233.

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Retinal Vessel Image Segmentation Based on RAU-net

1.
College of Computer Science and Technology, Changchun University of Technology, Changchun 130012, China
2.
College of Information Engineering, College of Optical And Electronical Information, Changchun University of Science and Technology, Changchun 130114, China

Received Date: 2020-10-08
Rev Recd Date: 2020-10-14
Publish Date: 2021-12-20

Abstract

Abstract

In the diagnosis of ophthalmic diseases, segmentation of retinal blood vessels is a quite effective method. However, using this method, difficulties in blood vessel segmentation are often encountered due to the low contrast of the retinal blood vessel background and complex details of the blood vessel end. Thus, residual learning is introduced by adding a basic U-net network to the process of network design. Through the introduction of residual learning and attention mechanism modules into the basic U-net network in the process of network design, a new type of U-net-based RAU-net retinal blood vessel image segmentation algorithm is proposed. First, the residual module is added to the encoder stage of the network to address gradient explosion and disappearance caused by the deepening of the model network. Second, the attention gate module is introduced in the decoder stage of the network to suppress unnecessary features to ensure high accuracy of the model. Through verification of DRIVE, the accuracy, sensitivity, specificity, and F₁-score of the algorithm reached 0.7832, 0.9815, 0.9568, and 0.8192, respectively. Thus, the segmentation effect was better than that of ordinary supervised learning algorithms
- image segmentation,
- retinal vessels,
- fully convolutional network,
- residual module,
- attention mechanism

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References(19)

References

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