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SAM-VMNet

Ocean University of China / Shandong University · 2025

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Code & model weights public

Segments coronary vessels from invasive X-ray angiography images and automatically quantifies the degree of stenosis along the extracted centerlines. Combines MedSAM, a Segment-Anything-style vision model, with a Mamba-based VM-UNet segmentation branch for efficient long-range feature modeling. Trained and evaluated on the ARCADE, DCA1, and GH angiography datasets by researchers at Ocean University of China and Shandong University.

Coronary angiography

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Invasive Coronary & Intracoronary Imaging

Coronary artery segmentation / anatomy

Filter by Disease / Trait:
Coronary & Ischemic Disease

Coronary artery disease / stenosis

Filter by Disease / Trait:
Coronary & Ischemic Disease

Segmentation

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Segmentation & Detection

Detection / localization

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Segmentation & Detection

Hybrid

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Hybrid / Multi-branch

PyTorch

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PyTorch

MIT

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Permissive


Model ID: 0001