Tubularity-Aware Self-Supervised Pretraining for 3D Vessel Segmentation

3D vessel segmentation requires delineating sparse, slender, and intricately branching tubular structures, yet acquiring annotations for learning these structures is costly. Self-supervised pretraining offers an opportunity to learn transferable representations from unlabeled images, but a key question remains: how can pretraining encourage vascular structure learning without manual annotations? Our premise is that, even when vessel locations are unknown, their elongated, curved morphology and fine-scale characteristics can guide the construction of self-supervised tasks. We propose Tubularity-Aware Pretraining (TAP), a self-supervised framework for 3D vessel segmentation that uses these priors to construct explicit restoration, localization, and detail prediction targets. Specifically, TAP combines cubic and curvilinear masking to learn contextual restoration of missing content; localizes intensity-matched donor content implanted into synthetic tubular regions to learn dense region discrimination; and predicts removed wavelet detail from coarse views to provide dedicated supervision for fine-scale information. Together, these tasks introduce explicit learning objectives for structural information relevant to vessel segmentation without requiring manual vessel annotations. Experiments demonstrate the effectiveness of TAP. Across four CT vessel datasets, TAP achieves state-of-the-art average DSC and clDice scores of 85.00% and 80.72%, respectively, outperforming the strongest competing method by 2.40 and 4.04 percentage points. Further experiments demonstrate its transferability to ASOCA, which is excluded from pretraining, and its effectiveness under limited-label fine-tuning. Code and models will be made publicly available.