HAU-Net: hybrid attention U-NET for retinal blood vessels image segmentation

Jialin Chen; Chunmei Ma; Ying Li; Shuaikun Fan; Rui Shi; Xiping Yan

doi:10.1117/12.3000792

9 August 2023 HAU-Net: hybrid attention U-NET for retinal blood vessels image segmentation

Jialin Chen, Chunmei Ma, Ying Li, Shuaikun Fan, Rui Shi, Xiping Yan

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127821H (2023) https://doi.org/10.1117/12.3000792
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Accurate semantic segmentation of retinal images is very important for intelligent diagnosis of eye diseases. However, the large number of tiny blood vessels and the uneven distribution of blood vessels in the retina pose many challenges to the segmentation algorithm. In this paper, we propose a Hybrid Attention Fusion U-Net model (HAU-Net) for segmentation of retinal blood vessel images. Specifically, we use the U-NET network as the backbone network, and bridge attention is introduced into the network to improve the efficiency of vessel feature extraction. In addition, we introduce channel attention and spatial attention modules at the bottom of the network, to obtain coarse-to-fine feature representation of retinal vessel images, so as to improve the accuracy of vascular image segmentation. In order to verify the model's performance, we conducted extensive experiments on DRIVE and CHASE_DB1 datasets, and the accuracy reach 97.03% and 97.72%, respectively, which are better than CAR-UNet and MC-UNet.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jialin Chen, Chunmei Ma, Ying Li, Shuaikun Fan, Rui Shi, and Xiping Yan "HAU-Net: hybrid attention U-NET for retinal blood vessels image segmentation", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127821H (9 August 2023); https://doi.org/10.1117/12.3000792

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KEYWORDS

Image segmentation

Blood vessels

Feature extraction

Data modeling

Education and training

Batch normalization

Convolution

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