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    RCAR-UNet:基于粗糙通道注意力机制的视网膜血管分割网络

    RCAR-UNet:Retinal Vessels Segmentation Network Based on Rough Channel Attention Mechanism

    • 摘要: 眼底图像中视网膜血管的健康状况对早期诊断各种眼科疾病及糖尿病心脑血管疾病等具有重要意义,然而视网膜血管结构细微、边界模糊且分布不规则,对其进行准确分割存在较大的难度.针对视网膜血管的这些特征,提出一种粗糙通道注意力残差U型网——粗糙通道注意力残差U型网络(RCAR-UNet).该网络首先引入粗糙集理论中上下近似概念设计粗糙神经元;接着基于粗糙神经元构建粗糙通道注意力模块,该模块在U-Net跳跃连接中采用全局最大池化和全局平均池化构造上下近似神经元,并进行神经元间的加权求和,对所建立的通道依赖关系进行合理的粗糙化,该依赖关系不仅包含全局信息,同时具有局部特性,可有效实现对所提取视网膜血管特征的准确重标定;然后添加残差连接,将特征直接从低层传递给高层,有助于解决网络性能退化问题,并有效提取更加丰富的视网膜血管特征;最后为了验证所提视网膜分割网络的有效性,在3个眼底视网膜公开图像数据集上与U-Net,Attention U-Net等传统网络模型进行对比实验,实验结果表明,所提视网膜分割网络在血管分割准确率、灵敏度和相似度等方面具有较高的优越性.

       

      Abstract: The health of retinal vessels in fundus images is of great significance for the early diagnosis of various ophthalmic diseases and diabetic cardiovascular diseases, etc. However, the retinal blood vessels are delicate, distributed irregularly and the boundary is ambiguous. Therefore, it is difficult to accurately segment them. Based on the characteristics of retinal blood vessels, we propose a U-shaped network—rough channel attention residual U-Net (RCAR-UNet), which combines rough neurons and channel attention mechanism. Firstly, the network introduces the concept of upper and lower approximation in rough set theory to design rough neurons. Secondly, the rough channel attention module is constructed based on rough neurons, and the module uses global max pooling and global average pooling in U-Net skip connections to construct upper and lower approximation neurons, and performs weighted summation between neurons to reasonably rough the established channel dependencies, which not only contain global information but also have local characteristics, and can effectively achieve accurate rescaling of the extracted retinal vessels features. Then adding residual connections to transfer features directly from the lower to the higher layers, to help solve the network performance degradation problem and effectively extract richer retinal vascular features. Finally, in order to verify the effectiveness of the proposed RCAR-UNet model, comparison experiments are performed on three public fundus image datasets with traditional network models such as U-Net, Attention U-Net, etc. The results show that the RCAR-UNet model has high superiority in the accuracy, sensitivity and similarity of blood vessel segmentation.

       

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