Abstract: Automatic segmentation of retinal vessels plays an important role in computer-aided diagnosis of ophthalmology and cardiovascular diseases. Attention mechanism can improve the efficiency and accuracy of image feature extraction in classical neural network models, so attention mechanism is widely used in retinal vessel segmentation models. This paper firstly reviews the commonly used datasets and evaluation metrics for retinal vessel segmentation, subsequently, attention mechanisms are categorized into two types: selective attention mechanisms and self-attention mechanisms, based on their working principles. Meanwhile, according to the data domain of computer vision tasks, attention methods are divided into three categories: channel attention, spatial attention, and mixed attention. Combined with the task of retinal vessel segmentation, the paper highlights the specific applications of representative attention models of these three types and conducts performance comparisons and evaluations of relevant models. Finally, the problems of attention mechanism and the development trend in the future are discussed.

Key words: retinal vessel segmentation, neural network, attention mechanism, computer vision

摘要： 视网膜血管的自动分割在眼科和心血管疾病的计算机辅助诊断中发挥着重要作用。注意力机制能够提高经典神经网络模型对图像特征提取的效率和精度，因此注意力机制在视网膜血管分割模型中广泛使用。首先回顾了视网膜血管分割的常用数据集及评价指标，接着根据工作机理将注意力分为选择性注意力机制和自注意力机制两类；根据计算机视觉任务中的作用域将注意力方法分为通道注意力、空间注意力以及混合注意力三类，结合视网膜血管分割任务重点介绍了以上三类方法的代表性注意力模型的具体应用，并对相关模型进行性能对比和评价。最后，对注意力机制存在的问题以及未来的发展趋势进行了讨论。

关键词: 视网膜血管分割, 神经网络, 注意力机制, 计算机视觉