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vesselFM

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software/vesselfm

A foundation model for universal 3D blood vessel segmentation. Paper accepted @ CVPR25.

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
maintained
Maturity
deployed
Organization
unknown
Country
unknown
Documentation
unknown
Tags
blood-vessel-segmentation · foundation-models · medical-image-segmentation
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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    SeqSeg (Sequential Vessel Segmentation)

  • Hi-End-MAEfoundation-models · medical-image-segmentation

    [MedIA 2026] Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation

  • Large-Scale-Medicalfoundation-models · medical-image-segmentation

    [TPAMI 2026] Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

  • SASVifoundation-models · medical-image-segmentation

    SASVi - Segment Any Surgical Video (IPCAI 2025)

  • STU-Netfoundation-models · medical-image-segmentation

    The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.

  • STU-Netfoundation-models · medical-image-segmentation

    The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations) to date.

sources
  1. api.github.com/repos/bwittmann/vesselFM
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2025-11-26, 163 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/62.json→ .entries["vesselfm"]

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