[1]王文辉,刘彦隆.基于残差通道注意力的视网膜血管图像分割[J].智能系统学报,2023,18(6):1268-1274.[doi:10.11992/tis.202107063]
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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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]
基于残差通道注意力的视网膜血管图像分割
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第6期
页码:
1268-1274
栏目:
学术论文—智能系统
出版日期:
2023-11-05
- Title:
- Retinal vascular image segmentation based on residual channel attention
- Keywords:
- image processing; retinal blood vessel; channel attenrion; edge detection; sensitivity; double residual block; feature fusion; deep learning
- 分类号:
- TP391
- 摘要:
- 视网膜血管分割是诊断许多早期眼睛相关疾病的重要步骤。本文将整体嵌套边缘检测（holistically-nested edge detection，HED）网络应用于视网膜血管图像分割，并对该模型进行了一系列改进：针对现有方法识别边缘和精细血管能力不足的问题，引入了一种新的改进的高效通道注意(modified efficient chanel attention，MECA)模块，并且采用了双残差结构加深模型结构，提取更加精细的血管结构，为了防止模型加深产生过拟合问题，引入了结构化丢弃模块。为了进一步提高模型的灵敏度，本文在HED网络的特征融合阶段加入融合了MECA模块的短连接结构。实验表明，所提网络的灵敏度相比于目前最先进的方法有了明显提升，这说明本文所提方法具有最先进的识别视网膜血管的能力。
- 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.
- 参考文献/References:
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备注/Memo
收稿日期:2021-7-29。
作者简介:王文辉,硕士研究生,主要研究方向为计算机视觉、图像处理;刘彦隆,副教授,主要研究方向为计算机视觉、图像处理。主持和参与山西教育厅教学改革项目1项,发表学术论文20篇
通讯作者:刘彦隆.E-mail:forever620@outlook.com
更新日期/Last Update:
1900-01-01