Automated partitioning of retinal vessels depicted in fundus images is beneficial in the detection of specific ailments like hypertension and diabetes. However, retinal vessel images have the problems of a large semantic range, more spatial detail, and limited differentiation among the blood vessels and surroundings, which make vessel segmentation challenging. To overcome these obstacles, we designed a new U‐shaped network named SMP‐Net. First, we propose the sequencer‐convolution (SC) module to obtain the ability to extract both local and global features, thereby improving segmentation accuracy. The SC module is used to filter out shallow noise and enable the fusion of deep and shallow features in the maximum skip connection of the U‐shaped network. Then, the residual multi‐kernel pooling (MP) module is designed to obtain additional contextual information while also mitigating the loss of spatial information caused by constant pooling and convolution to improve vessel coherence. Finally, the pixel attention (PA) module redistributes the weight of each pixel using an element‐wise product multiplication operation. This increases the weight of the vascular feature pixels and improves the ability to identify blood vessels in blurred backgrounds. The proposed method has been demonstrated to be effective through sufficient experimentation on publicly available retinal datasets such as DRIVE, STARE, and CHASE_DB1.

中文翻译：

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基于U型网络的多尺度与注意力相结合的视网膜血管分割

眼底图像中描绘的视网膜血管的自动分区有利于检测高血压和糖尿病等特定疾病。然而，视网膜血管图像存在语义范围大、空间细节较多、血管与周围环境区分有限等问题，这使得血管分割具有挑战性。为了克服这些障碍，我们设计了一种新的 U 形网络，名为 SMP-Net。首先，我们提出了序列器卷积（SC）模块来获得提取局部和全局特征的能力，从而提高分割精度。SC模块用于滤除浅层噪声，并在U形网络的最大跳跃连接中实现深层和浅层特征的融合。然后，设计残差多核池化（MP）模块以获得额外的上下文信息，同时减轻常量池化和卷积引起的空间信息损失，以提高血管相干性。最后，像素注意力（PA）模块使用逐元素乘积运算重新分配每个像素的权重。这增加了血管特征像素的权重并提高了在模糊背景中识别血管的能力。通过对公开可用的视网膜数据集（例如 DRIVE、STARE 和 CHASE_DB1）进行充分的实验，已证明所提出的方法是有效的。