arXiv Computer Vision
Modeling Clinical Workflow for SYNTAX Scoring from Coronary Angiography Videos
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SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos
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CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography
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MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography
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arXiv AI
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ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation
ReG-SAM is a SAM-based framework designed for 2D vessel segmentation in medical images. It introduces reference graph prompt embeddings (GPEs) and vascular prototype embeddings (VPEs) to capture global spatial and fine-grained modality-specific vessel features, respectively. By building a modality-wise vascular database and learning these embeddings from reference masks, ReG-SAM consistently outperforms existing baselines across 19 datasets, especially on thin vessels.