**TL;DR**: VesselFM is a foundation model for universal 3D blood vessel segmentation. It is trained on three heterogeneous data sources: a large, curated annotated dataset, synthetic data generated through domain randomization, and data sampled from a flow matching-based deep generative model. These data sources provide enough diversity to enable vesselFM to achieve exceptional *zero*-shot blood vessel segmentation, even in completely unseen 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). --- ## 🟢 Updates 08/2026: We release checkpoints trained on a **broader training set including our evaluation set**, for potentially increased *zero*-shot performance. ## 🟢 Installation First, set up a conda environment and install dependencies: conda create -n vesselfm python=3.9 conda activate vesselfm pip install -e . ## 🟢 *Zero*-Shot Segmentation If you are solely interested in running vesselFM's inference script for *zero*-shot segmentation of data at hand, adjust the respecitve [config file](vesselfm/seg/configs/inference.yaml) (see `#TODO`) and run: python vesselfm/seg/inference.py Additional information on inference, pre-training, and fine-tuning are available [here](./vesselfm/seg). Checkpoints will be downloaded automatically and are also available on [Hugging Face 🤗](https://huggingface.co/bwittmann/vesselFM). ## 🟢 Data Sources We also provide individual instructions for generating our three proposed data sources. $\mathcal{D}_\text{drand}$: Domain randomized synthetic data ([here](./vesselfm/d_drand)). $\mathcal{D}_\text{flow}$: Synthetic data sampled from our flow matching-based deep generative model ([here](./vesselfm/d_flow)). $\mathcal{D}_\text{real}$: Real data curated from 17 annotated blood vessel segmentation datasets ([here](./vesselfm/d_real)). ## 🟢 Citing vesselFM If you find our work useful for your research, 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} } ``` ## 🟢 License Code in this repository is licensed under [GNU General Public License v3.0](LICENSE). Model weights are released under [Open RAIL++-M License](https://huggingface.co/bwittmann/vesselFM/blob/main/LICENSE) and are restricted to research and non-commercial use only. Model use must comply with potential licenses, regulations, and restrictions arising from the use of named data sets during model training.