\section{Conclusion}
In this paper, we presented a novel framework that adapts the Segment Anything Model for 3D volumetric vessel segmentation. By integrating a geometric continuity constraint and a global consistency mechanism, we effectively resolved the slice-to-slice inconsistency problem inherent in 2D-to-3D adaptation.
Moreover, our proposed learnable offset prediction and confidence-guided refinement modules eliminate the need for manual interaction, enabling fully automated and robust vessel tracking. 
Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches in both segmentation accuracy and topological fidelity. 
We believe this work provides a scalable and effective solution for leveraging 2D foundation models in 3D medical image analysis.

\noindent\textbf{Limitations and Future Work.} Although the refinement module reduces error propagation, the slice-by-slice processing within our model remains inherently susceptible to it. Meanwhile, the slice-by-slice processing also prevents the method from recovering vessel segmentation that turns back into earlier slices. Additionally, handling complex bifurcations where a single vessel splits into multiple branches remains a challenge for the current single-point prompting mechanism. Future work will focus on integrating a multi-hypothesis tracking approach to handle bifurcations more effectively and exploring bi-directional propagation to further reduce error accumulation.
