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VesselBridge3D framework adapts foundation models for low-data 3D vessel segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed VesselBridge3D, a new framework designed to improve 3D vessel segmentation in medical imaging, particularly in low-data scenarios. This framework adapts existing foundation models, such as DINOv3, MedSAM, and MedGemma, using lightweight 3D adaptation modules. VesselBridge3D demonstrated significant performance gains, achieving a 30% relative improvement over state-of-the-art methods with only five training samples and showing superior robustness against domain shifts. AI

IMPACT Enhances medical imaging analysis capabilities in data-scarce environments, potentially improving diagnostic accuracy and reducing annotation costs.

RANK_REASON The cluster contains an arXiv preprint detailing a new framework and methodology for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VesselBridge3D framework adapts foundation models for low-data 3D vessel segmentation

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The cluster contains an arXiv preprint detailing a new framework and methodology for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Kirato Yoshihara, Yohei Sugawara, Yuta Tokuoka, Lihang Hong ·

    VesselBridge3D: A Foundation Model Adaptation Framework for Label-Efficient 3D Vessel Segmentation

    arXiv:2602.23782v2 Announce Type: replace-cross Abstract: State-of-the-art vessel segmentation methods typically require large-scale annotated datasets and suffer from severe performance degradation under domain shifts. In clinical practice, however, acquiring extensive annotatio…

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