Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity and by computer scientists to create computer-aided diagnostic systems to help in such assessment. In addition, baseline classification methods are proposed and analyzed, validating the functionality of CADICA and giving the scientific community a starting point to improve CAD detection.

翻译：冠状动脉疾病（CAD）仍是全球首要死因，当怀疑存在CAD时，有创冠状动脉造影（ICA）被视为解剖影像学评估的金标准。然而，基于ICA的风险评估存在诸多局限，如狭窄程度的视觉评估存在显著的观察者间变异性。这促使人们开发能够辅助临床诊疗流程的病变分类系统。尽管深度学习分类方法在其他医学影像领域已相当成熟，但ICA图像分类仍处于早期阶段。其中最重要的原因之一在于缺乏高质量的开源公开数据集。本文报告了一个新的标注ICA图像数据集CADICA，为研究界提供了一套包含患者视频采集数据及关联疾病元数据的综合性、严谨性冠状动脉造影数据集。该数据集可供临床医师训练其评估CAD严重程度的血管造影判读技能，亦可供计算机科学家开发辅助此类评估的计算机辅助诊断系统。此外，本文提出并分析了基线分类方法，验证了CADICA的功能性，为科学界改进CAD检测提供了研究起点。