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）评分是预防冠状动脉疾病风险评估的关键手段。既往针对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，且多基于预训练模型实现。然而，这些模型受限于含CAC标注的CT扫描数据稀缺及数据集不平衡问题，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，以克服CT扫描中标注数据匮乏的局限性。DINO模型无需CAC区域标注即可训练的特性，增强了其生成差异化特征的鲁棒性。通过引入标签引导，使DINO模型能够针对性聚焦钙化区域，旨在生成能有效捕捉并突出关键特征的表征。标签引导型DINO（DINO-LG）通过区分含钙化与不含钙化的CT切片来提升分类性能，在此任务上较标准DINO模型提升57%。CAC评分与分割任务由基础U-NET架构完成，其输入经DINO-LG模型特异性筛选为含钙化区域的CT切片。DINO-LG模型的定向识别机制使CAC分割性能提升约10%，并显著提高了CAC评分准确率。