Coronary artery disease (CAD), one of the most common cause of mortality in the world. Coronary artery calcium (CAC) scoring using computed tomography (CT) is key for risk assessment to prevent coronary disease. Previous studies on risk assessment and calcification detection in CT scans primarily use approaches based on UNET architecture, frequently implemented on pre-built models. However, these models are limited by the availability of annotated CT scans containing CAC and suffering from imbalanced dataset, decreasing performance of CAC segmentation and scoring. In this study, we extend this approach by incorporating the self-supervised learning (SSL) technique of DINO (self-distillation with no labels) to eliminate limitations of scarce annotated data in CT scans. The DINO model's ability to train without requiring CAC area annotations enhances its robustness in generating distinct features. The DINO model is trained on to focus specifically on calcified areas by using labels, aiming to generate features that effectively capture and highlight key characteristics. The label-guided DINO (DINO-LG) enhances classification by distinguishing CT slices that contain calcification from those that do not, performing 57% better than the standard DINO model in this task. CAC scoring and segmentation tasks are performed by a basic U-NET architecture, fed specifically with CT slices containing calcified areas as identified by the DINO-LG model. This targeted identification performed by DINO-LG model improves CAC segmentation performance by approximately 10% and significant increase in CAC scoring accuracy.

翻译：冠状动脉疾病（CAD）是全球最常见的致死病因之一。基于计算机断层扫描（CT）的冠状动脉钙化（CAC）评分是预防冠心病风险评估的关键手段。既往关于CT扫描中风险评估与钙化检测的研究主要采用基于UNET架构的方法，通常基于预训练模型实现。然而，这些模型受限于包含CAC的标注CT扫描数据稀缺，且面临数据集不平衡问题，导致CAC分割与评分性能下降。本研究通过引入DINO（无标签自蒸馏）自监督学习技术，以消除CT扫描中标注数据匮乏的限制。DINO模型无需CAC区域标注即可训练的能力，增强了其生成显著特征的鲁棒性。该模型通过标签引导专门聚焦于钙化区域，旨在生成能有效捕捉并突出关键特性的特征表示。标签引导型DINO（DINO-LG）通过区分包含钙化与无钙化的CT切片，在此任务中比标准DINO模型性能提升57%，从而增强了分类能力。CAC评分与分割任务由基础U-NET架构执行，其输入经DINO-LG模型特异性筛选出的含钙化区域CT切片。DINO-LG模型实现的靶向识别使CAC分割性能提升约10%，并显著提高了CAC评分准确度。