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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.08135 |
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| _version_ | 1866914379841142784 |
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| author | Mita, Soichi Takezaki, Shumpei Bise, Ryoma |
| author_facet | Mita, Soichi Takezaki, Shumpei Bise, Ryoma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images Mita, Soichi Takezaki, Shumpei Bise, Ryoma Computer Vision and Pattern Recognition 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. |
| title | VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.08135 |