\section{Conclusion}
\label{sec:conclusion}
In this work, we presented a novel method for extracting vessel graphs from 3D images.
We demonstrated its effectiveness on four datasets, comprising synthetic and real data, as well as single-tree and multi-tree vascular structures.
In addition, we introduced a hierarchical graph matching algorithm that yields more topologically meaningful node and edge correspondences, and we defined false splits and false merges as intuitive topology-aware error measures for tree graphs.
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Despite these advances, several evaluation metrics such as the edge-wise F1 score remain sensitive to different node sampling and matching strategies.
A more systematic analysis of these effects represents a valuable direction for future work.
Moreover, there is a pressing need for publicly available, real-world datasets with high-quality, manually annotated 3D vessel graphs.
Such datasets, combined with an established evaluation protocol encompassing sampling, matching, and metrics, would be essential for enabling consistent benchmarking and further methodological development in this area.