VesselFM

• vesselFM_all.pt: VesselFM pre-trained on a version of our three proposed data sources (D_real, D_drand, and D_flow), including our evaluation set for potential increased performance. This checkpoint will be automatically downloaded in vesselfm/seg/inference.py.

• vesselFM_all_ema.pt: EMA version of vesselFM_all.pt.

• vesselFM_base.pt: VesselFM pre-trained on a version of our three proposed data sources (D_real, D_drand, and D_flow). Performance is similar to the model reported in the manuscript.

Citing vesselFM

If you find our work useful, please cite:

@InProceedings{Wittmann_2025_CVPR,
    author    = {Wittmann, Bastian and Wattenberg, Yannick and Amiranashvili, Tamaz and Shit, Suprosanna and Menze, Bjoern},
    title     = {vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20874-20884}
}

vesselFM_base.pt

pt · 120 MB · SHA-256 6da4fbd06dd9…e10c · Hugging Face

--- pipeline_tag: image-segmentation tags: - medical - biology - clinical license: other # license_name: openrail++-m --- # vesselFM **TL;DR**: VesselFM is a foundation model for universal 3D blood vessel segmentation in arbitrary imaging domains. For details, please refer to our [manuscript](https://openaccess.thecvf.com/content/CVPR2025/html/Wittmann_vesselFM_A_Foundation_Model_for_Universal_3D_Blood_Vessel_Segmentation_CVPR_2025_paper.html) and our [GitHub repo](https://github.com/bwittmann/vesselFM). --- ### Checkpoints We provide the following checkpoints: - `vesselFM_all.pt`: VesselFM pre-trained on a version of our three proposed data sources (D_real, D_drand, and D_flow), *including our evaluation set* for potential increased performance. This checkpoint will be automatically downloaded in `vesselfm/seg/inference.py`. - `vesselFM_all_ema.pt`: EMA version of `vesselFM_all.pt`. - `vesselFM_base.pt`: VesselFM pre-trained on a version of our three proposed data sources (D_real, D_drand, and D_flow). Performance is similar to the model reported in the manuscript. ### Citing vesselFM If you find our work useful, please cite: ```bibtex @InProceedings{Wittmann_2025_CVPR, author = {Wittmann, Bastian and Wattenberg, Yannick and Amiranashvili, Tamaz and Shit, Suprosanna and Menze, Bjoern}, title = {vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {20874-20884} } ```