Vessel image segmentation plays a pivotal role in medical diagnostics, aiding in the early detection and treatment of vascular diseases. While segmentation based on deep learning has shown promising results, effectively segmenting small structures and maintaining connectivity between them remains challenging. To address these limitations, we propose GAEI-UNet, a novel model that combines global attention and elastic interaction-based techniques. GAEI-UNet leverages global spatial and channel context information to enhance high-level semantic understanding within the U-Net architecture, enabling precise segmentation of small vessels. Additionally, we adopt an elastic interaction-based loss function to improve connectivity among these fine structures. By capturing the forces generated by misalignment between target and predicted shapes, our model effectively learns to preserve the correct topology of vessel networks. Evaluation on retinal vessel dataset -- DRIVE demonstrates the superior performance of GAEI-UNet in terms of SE and connectivity of small structures, without significantly increasing computational complexity. This research aims to advance the field of vessel image segmentation, providing more accurate and reliable diagnostic tools for the medical community. The implementation code is available on Code.

翻译：血管图像分割在医学诊断中发挥着关键作用，有助于血管疾病的早期检测与治疗。尽管基于深度学习的分割方法已取得显著成果，但有效分割细小结构并维持其连通性仍具挑战性。为解决这些局限，我们提出GAEI-UNet——一种融合全局注意力与弹性交互技术的新型模型。GAEI-UNet利用全局空间与通道上下文信息增强U-Net架构中的高层语义理解，从而实现对微小血管的精准分割。此外，我们采用基于弹性交互的损失函数改善这些精细结构的连通性。通过捕捉目标形状与预测形状错位产生的力学效应，该模型能够有效学习保留血管网络的正确拓扑结构。在视网膜血管数据集DRIVE上的评估表明，GAEI-UNet在细小结构的敏感度与连通性方面表现出优异性能，且未显著增加计算复杂度。本研究旨在推动血管图像分割领域发展，为医学界提供更准确可靠的诊断工具。实现代码已开源。