[2603.20021] ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images

Computer Science > Machine Learning arXiv:2603.20021 (cs) [Submitted on 20 Mar 2026] Title:ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images Authors:Anand Choudhary, Xiaowu Sun, Thabo Mahendiran, Ortal Senouf, Denise Auberson, Bernard De Bruyne, Stephane Fournier, Olivier Muller, Emmanuel Abbé, Pascal Frossard, Dorina Thanou View a PDF of the paper titled ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images, by Anand Choudhary and 10 other authors View PDF HTML (experimental) 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 Minimu...