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

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.

Figures (8)

• Figure 1: Overview of ODySSeI. (a) During inference, a raw ICA image undergoes detection, segmentation (after cropping and resizing the lesion instances detected in the image), and severity estimation of any lesions present in it. (b) Raw training data is constantly augmented using all the three tiers of our Pyramidal Augmentation Scheme (PAS) to iteratively fine-tune our lesion detection model for the identification of lesions and prediction of their bounding box coordinates. The fine-tuned lesion detection model is then integrated into ODySSeI for inference. (c) Cropped training data (only lesion instances) is constantly augmented using two tiers of our PAS to iteratively fine-tune our lesion segmentation model for the prediction of lesion geometry. The fine-tuned lesion segmentation model is then integrated into ODySSeI for inference. (d) The three tiers of our PAS: static, dynamic, and composite augmentations. (e) Our lesion severity estimation technique involves computing the arterial radius of every point on the centerline skeleton of the segmented lesion. Twice of the minimum between two radii peaks yields the Minimum Lumen Diameter (MLD). Complement of the ratio of the MLD to the highest peak's diameter yields the Diameter Stenosis (DS).
• Figure 2: PAS-based data augmentation changes the learning dynamics of ODySSeI's lesion detection model and encourages structured overfitting via static augmentations, imparts robustness and stabilization via dynamic augmentations, and improves precision in lesion localization via composite augmentations. Plots show (a) lesion-level mAP@0.50 on the FAME2 validation set, (b) lesion-level mAP@0.50-0.95 on the FAME2 validation set, and (c) loss on the FAME2 validation set.
• Figure 3: Lesion Detection using ODySSeI: Visualization of predictions and the corresponding ground truths on a representative sample of the FAME2 validation set show the improved robustness of our best performing lesion detection model over the baseline.
• Figure 4: Lesion Detection using ODySSeI: Visualization of predictions and the corresponding ground truths on a representative sample of the FAME2 test set show that our lesion detection model identifies and localizes lesions accurately and in some cases, identifies lesions which are missing from the corresponding ground truths.
• Figure 5: Lesion Detection using ODySSeI: Visualization of predictions and the corresponding ground truths on a representative sample of the FC dataset show that our model identifies and precisely localizes most of the narrow lesions while occasionally missing out on identifying some wide lesions.