[1]WANG Wenhui,LIU Yanlong.Retinal vascular image segmentation based on residual channel attention[J].CAAI Transactions on Intelligent Systems,2023,18(6):1268-1274.[doi:10.11992/tis.202107063]
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Retinal vascular image segmentation based on residual channel attention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1268-1274 Column: 学术论文—智能系统 Public date: 2023-11-05
Title:
Retinal vascular image segmentation based on residual channel attention
Author(s):
WANG Wenhui;  LIU Yanlong
School of Information and Computer Science, Taiyuan University of Technology, Jinzhong 030600, China
Keywords:
image processing; retinal blood vessel; channel attenrion; edge detection; sensitivity; double residual block; feature fusion; deep learning
CLC:
TP391
DOI:
10.11992/tis.202107063
Abstract:
Segmentation of retinal blood vessels is an important step in the diagnosis of many early eye-related diseases. In this paper, the holistically-nested edge detection (HED) network is applied to retinal vascular image segmentation, and a series of improvements are made to the model: a new modified efficient channel attention (MECA) module is introduced to address the lack of ability of existing methods to identify edges and fine vessels, and a double residual structure is used to deepen the model structure to extract finer vascular structures. A structured DropBlock module is introduced to prevent overfitting problems from model deepening. In order to further improve sensitivity of the model, a short connection structure incorporating the MECA module is added in the feature fusion phase of the HED network. Experiments show that compared with the current state-of-the-art methods, the sensitivity of the proposed network is significantly improved, which indicates that the proposed method has the state-of-the-art ability to identify retinal vessels.
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