Structural congruence matters: Biological pretraining for vascular graph extraction

Vascular graph extraction converts medical images into structured representations of vessel connectivity, enabling quantitative analysis of branching topology. However, training image-to-graph models requires expert-annotated vascular graphs, creating a substantial annotation bottleneck. Recent work leverages large non-medical datasets for structural pretraining, yet the structural alignment between source and vascular domains remains underexplored. We introduce a nature-to-nature pretraining paradigm that uses botanical branching networks as a biologically aligned source domain for vascular graph extraction. Leveraging large-scale annotated grapevine skeleton graphs and geometry-preserving graph representations, our approach promotes relational priors consistent with vascular growth and connectivity. Across heterogeneous 2D and 3D benchmarks, botanical pretraining consistently outperforms infrastructure-based pretraining under identical training settings. In addition, biologically aligned supervision achieves comparable performance with substantially fewer annotated vascular volumes. Multi-domain training further enhances robustness to previously unseen vascular datasets. These findings demonstrate that structural congruence between source and target domains provides a stronger inductive bias for vascular graph extraction than scale alone. Code available at: https://github.com/erc-caravel/Vascular-Graph-Extraction.