Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. In general, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion in technique. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known U-net with encoder–decoder architecture, which is widely employed in medical image segmentation. In the network structure, cascaded dilated convolutional modules have been integrated into the intermediate layers, for obtaining larger receptive field and generating denser encoded feature maps. Also, the advantages of the pyramid module with spatial continuity have been taken for multi-thickness perception, detail refinement, and contextual feature fusion. Additionally, the effectiveness of different normalization approaches has been discussed on different datasets with specific properties. Finally, sufficient comparative experiments have been enforced on three retinal vessel segmentation datasets, DRIVE, CHASE_DB1, and the STARE dataset with unhealthy samples. As a result, the proposed method outperforms the work of predecessors and achieves state-of-the-art performance.

在彩色眼底图像分析中，准确的视网膜血管分割是一个具有挑战性的问题。自动视网膜血管分割系统可以有效地促进临床诊断和眼科研究。通常，此问题受到不同程度的血管厚度，对细节的感知以及技术中的上下文特征融合的困扰。为了解决这些挑战，已经提出了一种基于深度学习的方法，并且已将几种定制模块集成到具有编码器-解码器体系结构的著名U-net中，该体系已广泛用于医学图像分割中。在网络结构中，级联的扩展卷积模块已集成到中间层中，以获得更大的接收场并生成更密集的编码特征图。还，金字塔模块具有空间连续性的优势已被用于多厚度感知，细节细化和上下文特征融合。此外，已针对具有特定属性的不同数据集讨论了不同归一化方法的有效性。最后，已经对三个视网膜血管分割数据集DRIVE，CHASE_DB1和带有不良样品的STARE数据集进行了足够的比较实验。结果，所提出的方法优于以前的工作并获得了最新的性能。已经对三个视网膜血管分割数据集DRIVE，CHASE_DB1和带有不良样品的STARE数据集进行了足够的比较实验。结果，所提出的方法优于以前的工作并获得了最新的性能。已经对三个视网膜血管分割数据集DRIVE，CHASE_DB1和带有不良样品的STARE数据集进行了足够的比较实验。结果，所提出的方法优于以前的工作并获得了最新的性能。