Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to estimate natural vessel structures since conventional approaches are deterministic models, which cannot capture a complex human structure. In this study, we propose VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image. The proposed method uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction. VesselFusion was evaluated on a publicly available CT image dataset and achieved higher extraction accuracy and a more natural result than conventional approaches.
@article{arxiv.2603.08135,
title = {VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images},
author = {Soichi Mita and Shumpei Takezaki and Ryoma Bise},
journal= {arXiv preprint arXiv:2603.08135},
year = {2026}
}