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）仍是全球首要致死原因，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的风险评估存在若干局限性，例如对狭窄严重程度的视觉评估存在显著观察者间差异。这促使人们开发一种能够支持临床专家诊疗流程的病变分类系统。尽管深度学习分类方法在医学影像其他领域已发展成熟，ICA图像分类仍处于早期阶段，其中一个重要原因是缺乏高质量且可公开获取的数据集。本文报告了一个新的标注ICA图像数据集CADICA，为研究社区提供了一套包含患者采集视频及相关疾病元数据的综合性、严谨性冠状动脉造影数据集。该数据集既可帮助临床医生训练其在CAD严重程度血管造影评估中的技能，也可供计算机科学家创建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，并为科学界改善CAD检测提供了研究起点。