Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra- and inter-operator variability. In this work, we introduce ODySSeI: an Open-source end-to-end framework for automated Detection, Segmentation, and Severity estimation of lesions in ICA images. ODySSeI integrates deep learning-based lesion detection and lesion segmentation models trained using a novel Pyramidal Augmentation Scheme (PAS) to enhance robustness and real-time performance across diverse patient cohorts (2149 patients from Europe, North America, and Asia). Furthermore, we propose a quantitative coronary angiography-free Lesion Severity Estimation (LSE) technique that directly computes the Minimum Lumen Diameter (MLD) and diameter stenosis from the predicted lesion geometry. Extensive evaluation on both in-distribution and out-of-distribution clinical datasets demonstrates ODySSeI's strong generalizability. Our PAS yields large performance gains in highly complex tasks as compared to relatively simpler ones, notably, a 2.5-fold increase in lesion detection performance versus a 1-3\% increase in lesion segmentation performance over their respective baselines. Our LSE technique achieves high accuracy, with predicted MLD values differing by only $\pm$ 2-3 pixels from the corresponding ground truths. On average, ODySSeI processes a raw ICA image within only a few seconds on a CPU and in a fraction of a second on a GPU and is available as a plug-and-play web interface at swisscardia.epfl.ch. Overall, this work establishes ODySSeI as a comprehensive and open-source framework which supports automated, reproducible, and scalable ICA analysis for real-time clinical decision-making.

翻译：有创冠状动脉造影（ICA）是评估冠状动脉疾病的临床金标准。然而，其解读仍具有主观性，且易受操作者内部及操作者间变异性的影响。本研究提出ODySSeI：一种用于ICA图像中病变自动检测、分割及严重程度评估的开源端到端框架。ODySSeI集成了基于深度学习的病变检测与病变分割模型，这些模型采用新型金字塔增强方案（PAS）进行训练，以提升在不同患者队列（涵盖欧洲、北美及亚洲的2149名患者）中的鲁棒性与实时性能。此外，我们提出了一种无需定量冠状动脉造影的病变严重程度评估（LSE）技术，该技术可直接从预测的病变几何结构中计算最小管腔直径（MLD）及直径狭窄率。在分布内和分布外临床数据集上的广泛评估表明，ODySSeI具有较强的泛化能力。与相对简单的任务相比，我们的PAS在高度复杂任务中实现了显著的性能提升：具体而言，病变检测性能相较于基线提升了2.5倍，而病变分割性能相较于基线提升了1-3%。我们的LSE技术达到了高精度，预测的MLD值与相应的真实值仅相差±2-3个像素。平均而言，ODySSeI可在CPU上仅需数秒、在GPU上仅需不到一秒即可处理一张原始ICA图像，并可通过即插即用的网络界面在swisscardia.epfl.ch获取。总体而言，本研究将ODySSeI确立为一个全面且开源的框架，为实时临床决策支持自动、可重复且可扩展的ICA分析。