Abstract
Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively.
Highlights
Ophthalmology is an important research area of contemporary medicine
In view of the above issues, this paper proposes a retinal blood vessel segmentation model based on the deep fully convolutional neural network (FCN)
Compared with basic the FCN, the butterfly fully convolutional neural network (BFCN) has the following advantages: (i) multiscale input can effectively improve the quality of segmentation; (ii) using dilated convolution with different expansion rates to obtain larger receptive fields and rich spatial information is helpful in fully understanding local context information; and (iii) the transfer layer performs the global average pool on the output of the encoding path and calculates the attention vector to guide the feature map learning
Summary
Ophthalmology is an important research area of contemporary medicine. Eye health is closely related to people’s lives. ere is a wide variety of ophthalmic diseases, such as cataract, glaucoma, and diabetic retinopathy that have a high incidence, and diabetic retinopathy is one of the main causes of blindness [1]. Compared with basic the FCN, the BFCN has the following advantages: (i) multiscale input can effectively improve the quality of segmentation; (ii) using dilated convolution with different expansion rates to obtain larger receptive fields and rich spatial information is helpful in fully understanding local context information; and (iii) the transfer layer performs the global average pool on the output of the encoding path and calculates the attention vector to guide the feature map learning. It can improve the network’s sensitivity to information features.
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