vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
Open accessAbstract
Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging protocols, limit the generalization of existing supervised learning-based methods, requiring tedious voxel-level annotations for each dataset separately. While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem. In this work, we present vesselFM, a foundation model designed specifically for the broad task of 3D blood vessel segmentation. Unlike previous models, vesselFM can effortlessly generalize to unseen domains. To achieve zero-shot generalization, we train vesselFM on three heterogeneous data sources: a large, curated annotated dataset, data generated by a domain randomization scheme, and data sampled from a flow matching-based generative model. Extensive evaluations show that vesselFM outperforms state-of-the-art medical image segmentation foundation models across four (pre-)clinically relevant imaging modalities in zero-, one-, and few-shot scenarios, therefore providing a universal solution for 3D blood vessel segmentation.
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We developed the Nerve-Module of MouseMapper for nerve segmentation by fine-tuning a pretrained foundation model, VesselFM, using our curated dataset.
“We developed the Nerve-Module of MouseMapper for nerve segmentation by fine-tuning a pretrained foundation model, VesselFM14, using our curated dataset (described above).”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [14]
VesselFM was pretrained on a large-scale 3D vessel dataset and was originally designed for 3D blood vessel segmentation.
“VesselFM was pretrained on a large-scale 3D vessel dataset and was originally designed for the broad task of 3D blood vessel segmentation.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [14]
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- Authors
- Wittmann, B., Wattenberg, Y., Amiranashvili, T., Shit, S., & Menze, B.
- Affiliations
- University of Zurich (CH)
- ETH Zurich (CH)
- Venue
- 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2025-06-10
- Pages
- 20874-20884
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4413146896
- Cited by
- 25
- References
- 45
- Citations by year
- 2026: 23 · 2025: 2
- FWCI
- 18.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- foundation models, zero-shot generalization, domain randomization, domain gap, medical image segmentation
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