Anatomy-conditioned domain randomization for zero-shot 3D cerebrovascular segmentation

Domain randomization methods have shown strong cross-domain robustness in neuroimaging, yet their use for 3D cerebrovascular segmentation remains limited. Existing vascular synthetic pipelines largely model vessel morphology and appearance in isolation, with limited explicit control of organ-level anatomical context during image generation. We propose an anatomy-conditioned DR framework for zero-shot 3D cerebrovascular segmentation using only synthetic training images that addresses these challenges through three task-specific components: anatomically consistent background context, morphology-aware vessel enrichment, and contrast-aware randomized label-to-image synthesis. A 3D CNN is trained exclusively on synthetically generated images and corresponding vessel masks, with no target-domain images or masks used for training or model selection. Under strict zero-shot evaluation on unseen TOF-MRA and CTA datasets, our method improves over a vessel-centric synthetic baseline and matches or exceeds a vessel-specific foundation model on CTA, while remaining competitive on TOF-MRA. These results show that anatomy-conditioned DR can provide a label-efficient alternative to large foundation-model pipelines for robust zero-shot 3D cerebrovascular segmentation. Our code is available at https://github.com/erc-caravel/anatomical_vessel_dr.