论文标题
使用深度学习和特定于任务的中心线标签进行CAD-RADS得分
CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling
论文作者
论文摘要
由于冠状动脉疾病（CAD）持续成为全球死亡的主要原因之一，因此有兴趣支持使用算法加快诊断和改善诊断的医生。在临床实践中，经常通过冠状动脉血管造影（CCTA）扫描评估CAD的严重性，并通过CAD报告和数据系统（CAD-RADS）得分手动分级。该分数评估的临床问题是患者是否患有CAD（排除）以及他们是否患有严重的CAD（持有）。在这项工作中，我们达到了自动CAD-RADS得分的新最新性能。我们建议使用基于严重性的标签编码，测试时间扩展（TTA）和模型结合，以进行特定于任务的深度学习体系结构。此外，我们引入了一种新颖的任务和模型特异性的启发式冠状节段标记，该标签将冠状动脉树细化为患者的一致部分。它是快速，健壮且易于实现的。我们能够在排除率中将接收器操作特性曲线（AUC）下面报告的面积从0.914提高到0.942，分别在持有任务中从0.921提高到0.921。
With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. Furthermore, we introduce a novel task- and model-specific, heuristic coronary segment labeling, which subdivides coronary trees into consistent parts across patients. It is fast, robust, and easy to implement. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.
