Abstract
Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness. Most segmentation methods based on deep convolutional neural networks can effectively extract features. However, convolution and pooling operations also filter out some useful information, and the final segmented retinal vessels have problems such as low classification accuracy. In this paper, we propose a multi-scale residual attention network called MRA-UNet. Multi-scale inputs enable the network to learn features at different scales, which increases the robustness of the network. In the encoding phase, we reduce the negative influence of the background and eliminate noise by using the residual attention module. We use the bottom reconstruction module to aggregate the feature information under different receptive fields, so that the model can extract the information of different thicknesses of blood vessels. Finally, the spatial activation module is used to process the up-sampled image to further increase the difference between blood vessels and background, which promotes the recovery of small blood vessels at the edges. Our method was verified on the DRIVE, CHASE, and STARE datasets. Respectively, the segmentation accuracy rates reached 96.98%, 97.58%, and 97.63%; the specificity reached 98.28%, 98.54%, and 98.73%; and the F-measure scores reached 82.93%, 81.27%, and 84.22%. We compared the experimental results with some state-of-art methods, such as U-Net, R2U-Net, and AG-UNet in terms of accuracy, sensitivity, specificity, F-measure, and AUCROC. Particularly, MRA-UNet outperformed U-Net by 1.51%, 3.44%, and 0.49% on DRIVE, CHASE, and STARE datasets, respectively.
Highlights
Retinal image segmentation technology has been used in the diagnosis of cardiovascular and ophthalmic diseases [1], among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness [2]
To solve the above problems, this paper proposes a retinal vessel segmentation model based on convolutional neural network
In order to validate the effectiveness of the residual attention module (RA), bottom reconstruction module (BR), and spatial activation module (SA) proposed in this paper, experiments were carried out on DRIVE and CHASE datasets
Summary
Retinal image segmentation technology has been used in the diagnosis of cardiovascular and ophthalmic diseases [1], among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness [2]. In [5], a new multi-scale vessel enhancement method based on complex continuous wavelet transform (CCWT) was proposed, which uses histogram-based adaptive thresholding method and appropriate length filtering method to segment vessels. In [9], a hybrid method for retinal vessel segmentation was proposed. This method uses normalized Gabor filtering to reduce background brightness changes and alleviate the false positive problem caused by light changes. In [12], gray conversion based on principal component analysis (PCA) and contrast-limited adaptive histogram equalization (CLAHE) was used for retinal image enhancement, and a new matched filter was designed to segment the blood vessels
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