Segmentation of retinal blood vessels is important for the analysis of diabetic retinopathy (DR). Existing methods do not prioritize the small and disconnected vessels for DR. With the aim of paying attention to the small and disconnected vessel regions, this study introduced Euler characteristics (EC) from topology to calculate the number of isolated objects on segmented vessel regions, which is the key contribution of this study. In addition, we utilized the number of isolated objects in a U-Net-like deep convolutional neural network (CNN) architecture as a regularizer to train the network for improving the connectivity between the pixels of the vessel regions. The proposed network performance of the regularizer based on EC in reconstructing vessel regions is compared over the network without our regularizer. Furthermore, the capacity of the proposed regularizer approach in enhancing the smoothness and pixel connectivity of the vessels is compared with graph-based smoothing (GS) and combined GS with isolated objects (GISO) regularizers for delineating blood vessel regions. The proposed approach achieved the area under the curve value of 0.982, which is much higher than the state-of-the-arts, and thus it is suggested that the proposed system could support accuracy and reliability in decision-making for DR detection.

视网膜血管的分割对于糖尿病视网膜病变 (DR) 的分析很重要。现有的方法没有优先考虑 DR 的小型和断开连接的船只。本研究以关注小且不连续的血管区域为目的，从拓扑学中引入欧拉特征（EC）来计算分割血管区域上孤立对象的数量，这是本研究的关键贡献。此外，我们利用类似 U-Net 的深度卷积神经网络 (CNN) 架构中孤立对象的数量作为正则化器来训练网络，以改善血管区域像素之间的连通性。在没有我们的正则化器的情况下，在网络上比较了基于 EC 的正则化器在重建血管区域方面的建议网络性能。此外，将所提出的正则化方法在增强血管平滑度和像素连通性方面的能力与基于图的平滑 (GS) 和结合 GS 与孤立对象 (GISO) 正则化器进行比较，以描绘血管区域。所提出的方法实现了0.982的曲线下面积值，远高于现有技术，因此表明所提出的系统可以支持DR检测决策的准确性和可靠性。