RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism
RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism
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DOI：
10.1016/j.ins.2023.120007
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Weiping Ding;Ying Sun;Jiashuang Huang;Hengrong Ju;Chongsheng Zhang;Guan Yang;Chin-Teng Lin
中科院分区：
文献类型：
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作者：
Weiping Ding;Ying Sun;Jiashuang Huang;Hengrong Ju;Chongsheng Zhang;Guan Yang;Chin-Teng Lin
视网膜血管的健康状况是快速、无创诊断各种眼科、糖尿病和心脑血管疾病的重要参考。然而，视网膜血管的特征在于边界模糊，具有多个厚度和模糊的病变区域。这些现象导致深度神经网络在分割视网膜血管时面临特征通道不确定性。特征通道的不确定性会影响通道注意系数，使深度神经网络无法关注视网膜血管的细节特征。本研究提出一种透过粗通道注意机制的视网膜血管分割方法。首先，该方法集成了深度神经网络来学习复杂特征，并集成了粗糙集来处理粗糙神经元设计的不确定性。其次，基于粗神经元构建粗通道注意机制模块，并将其嵌入到U-Net跳跃连接中，实现了高、低层特征的融合。然后，在训练模型时，加入残差连接，将低层特征传递到高层，丰富网络特征提取，并帮助反向传播梯度。最后，在三个公开的眼底视网膜图像数据集上进行了多重比较实验，验证了粗糙通道注意力残差U网（RCAR-UNet）模型的有效性。结果表明，RCAR-UNet模型在准确性、灵敏度、F1和Jaccard相似度等方面具有较高的优势，尤其是对于脆弱血管的精确分割，保证了血管的连续性。
The health status of the retinal blood vessels is a significant reference for rapid and non-invasive diagnosis of various ophthalmological, diabetic, and cardio-cerebrovascular diseases. However, retinal vessels are characterized by ambiguous boundaries, with multiple thicknesses and obscured lesion areas. These phenomena cause deep neural networks to face the characteristic channel uncertainty when segmenting retinal blood vessels. The uncertainty in feature channels will affect the channel attention coefficient, making the deep neural network incapable of paying attention to the detailed features of retinal vessels. This study proposes a retinal vessel segmentation via a rough channel attention mechanism. First, the method integrates deep neural networks to learn complex features and rough sets to handle uncertainty for designing rough neurons. Second, a rough channel attention mechanism module is constructed based on rough neurons, and embedded in U-Net skip connection for the integration of high-level and low-level features. Then, the residual connections are added to transmit low-level features to high-level to enrich network feature extraction and help back-propagate the gradient when training the model. Finally, multiple comparison experiments were carried out on three public fundus retinal image datasets to verify the validity of Rough Channel Attention Residual U-Net (RCAR-UNet) model. The results show that the RCAR-UNet model offers high superiority in accuracy, sensitivity, F1, and Jaccard similarity, especially for the precise segmentation of fragile blood vessels, guaranteeing blood vessels’ continuity.