无需额外标注，跨域实现心冠钙化自动评分
A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography
- 用自监督ViT模型在有门控数据上训练，直接用于无门控扫描
- 无门控数据训练下准确率0.707，与专用模型相当
- 适合临床常规胸部扫描中大规模心血管风险筛查
冠状动脉钙化（CAC）评分是心血管风险的重要预测指标，但传统依赖心电图门控CT，限制了其在普通影像中的应用。本文提出一种自动化框架，可在有门控和无门控CT扫描间实现跨域CAC检测与病变特异性Agatston评分。核心为CARD-ViT，一种仅在门控数据上使用DINO进行自监督训练的视觉变换器。该框架未使用任何无门控训练数据，在斯坦福无门控数据集上达到0.707准确率和0.528的Cohen's kappa，与直接在无门控数据上训练的模型表现相当。在有门控测试集上，准确率达0.910，kappa值分别为0.871和0.874，展现出稳健的风险分层能力。结果表明，从门控到无门控的跨域CAC评分可行，支持在常规胸部影像中实现可扩展的心血管筛查，无需额外扫描或标注。
原文摘要 · Abstract (English)
Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an automated framework for CAC detection and lesion-specific Agatston scoring that operates across both gated and non-gated CT scans. At its core is CARD-ViT, a self-supervised Vision Transformer trained exclusively on gated CT data using DINO. Without any non-gated training data, our framework achieves 0.707 accuracy and a Cohen's kappa of 0.528 on the Stanford non-gated dataset, matching models trained directly on non-gated scans. On gated test sets, the framework achieves 0.910 accuracy with Cohen's kappa scores of 0.871 and 0.874 across independent datasets, demonstrating robust risk stratification. These results demonstrate the feasibility of cross-domain CAC scoring from gated to non-gated domains, supporting scalable cardiovascular screening in routine chest imaging without additional scans or annotations.
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