  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.
