ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images

2026-03-20

AI Summary

This study addresses the high subjectivity and inter-operator variability in lesion detection, segmentation, and stenosis severity assessment in coronary angiography by proposing an end-to-end open-source framework. Integrating deep learning models with a novel Pyramid Augmentation Strategy (PAS), the framework substantially enhances generalization and real-time performance. It introduces, for the first time, a quantitative coronary angiography (QCA)-free algorithm that directly estimates minimal lumen diameter (MLD) and stenosis percentage from predicted lesion geometry. Evaluated on multicenter clinical data, the method achieves a 2.5-fold improvement in lesion detection accuracy, with MLD prediction errors of only ±2–3 pixels. Inference on a single image completes within seconds on a CPU, and the system includes a plug-and-play web interface for immediate clinical deployment.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Invasive Coronary Angiography
• Lesion Detection
• Lesion Segmentation
• Severity Estimation
• Inter-operator Variability

Innovation

• Pyramidal Augmentation Scheme
• Lesion Severity Estimation
• End-to-End Framework
• Minimum Lumen Diameter
• Invasive Coronary Angiography