Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary artery calcium (CAC) scoring via computed tomography (CT) being a key method for prevention. Traditional methods, primarily based on UNET architectures implemented on pre-built models, face challenges like the scarcity of annotated CT scans containing CAC and imbalanced datasets, leading to reduced performance in segmentation and scoring tasks. In this study, we address these limitations by introducing DINO-LG, a novel label-guided extension of DINO (self-distillation with no labels) that incorporates targeted augmentation on annotated calcified regions during self-supervised pre-training. Our three-stage pipeline integrates Vision Transformer (ViT-Base/8) feature extraction via DINO-LG trained on 914 CT scans comprising 700 gated and 214 non-gated acquisitions, linear classification to identify calcified slices, and U-NET segmentation for CAC quantification and Agatston scoring. DINO-LG achieved 89% sensitivity and 90% specificity for detecting CAC-containing CT slices, compared to standard DINO's 79% sensitivity and 77% specificity, reducing false-negative and false-positive rates by 49% and 57% respectively. The integrated system achieves 90% accuracy in CAC risk classification on 45 test patients, outperforming standalone U-NET segmentation (76% accuracy) while processing only the relevant subset of CT slices. This targeted approach enhances CAC scoring accuracy by feeding the UNET model with relevant slices, improving diagnostic precision while lowering healthcare costs by minimizing unnecessary tests and treatments.

翻译：冠状动脉疾病（CAD）是全球主要致死原因之一，需要有效的风险评估策略，其中通过计算机断层扫描（CT）进行冠状动脉钙化（CAC）评分是关键的预防手段。传统方法主要基于在预建模型上实现的UNET架构，面临着包含CAC的标注CT扫描稀缺以及数据集不平衡等挑战，导致分割和评分任务的性能下降。在本研究中，我们通过引入DINO-LG来解决这些局限性，这是一种新颖的标签引导扩展DINO（无标签自蒸馏）方法，在自监督预训练期间对标注的钙化区域进行针对性增强。我们的三阶段流程包括：通过DINO-LG在包含700门控和214非门控采集的914个CT扫描上进行训练，实现基于Vision Transformer（ViT-Base/8）的特征提取；线性分类以识别钙化切片；以及U-NET分割用于CAC量化和Agatston评分。DINO-LG在检测包含CAC的CT切片方面达到了89%的敏感性和90%的特异性，而标准DINO的敏感性和特异性分别为79%和77%，将假阴性和假阳性率分别降低了49%和57%。该集成系统在45名测试患者中实现了CAC风险分类90%的准确率，优于独立的U-NET分割（76%准确率），同时仅处理相关的CT切片子集。这种针对性方法通过向UNET模型提供相关切片，提高了CAC评分的准确性，从而提升了诊断精度，同时通过减少不必要的检测和治疗降低了医疗成本。