Background Quantitative coronary angiography (QCA) permits the objective assessment of coronary artery disease. However, traditional edge detection algorithms often struggle with accurate vessel segmentation due to the complexity and variable quality of angiographic images. As a result, manual correction is frequently needed, which is a major limitation of current QCA systems. Purpose We developed angioPy, a deep learning-driven tool for vessel segmentation that employs user-defined ground truth points to minimise manual correction. We compared its performance without correction with an established QCA system. Methods angioPy was developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. Deep learning models were constructed using the U-Net architecture with one of three popular backbones for image classification: ResNet101, DenseNet121, and InceptionResNet-v2. Critically, the model was constructed to integrate the user’s clinical expertise through the selection of a 5-10 ground-truth points along the length of the vessel of interest including the desired start and end point of vessel segmentation. These ground truth points were added to a separate channel of the input image. Five-fold cross validation was performed using proportions of 3:1:1 for training, validation, and test sets, respectively. External validation was performed on a separate dataset of 580 images not used for model development. For both datasets, ground truth segmentation masks were provided by interventional cardiologists using specialised in-house software. A separate analysis was performed in a cohort of 74 patients with mild/moderate stenoses that underwent vessel segmentation using both angioPy and an established commercial QCA package (Medis QFR®). Vessel dimensions at stenoses (proximal and distal reference, minimal luminal diameter) were extracted from both segmentation approaches and compared (609 diameters in total). Results The best performing model performed segmentation with an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2% of masks exhibiting an F1 score > 0.8 (Figure 1). Simliar results were achieved in the external validation set (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Stenosis dimensions measured with angioPy (mean diameter 3.54 ±1.15 mm) exhibited excellent agreement with QCA (r=0.95 [95% CI 0.95-0.96], p<0.001; mean difference -0.18 mm [limits of agreement: -0.84 to 0.49]) (Figure 2). Minimal luminal diameter (mean 2.93 ±0.91 mm) also exhibited strong agreement (r=0.93 [95% CI 0.91-0.95], p<0.001; mean difference -0.06 mm [limits of agreement: -0.70 to 0.59]. Conclusion angioPy performs rapid and accurate coronary segmentation without the need for manual correction. Available as an open-source tool, angioPy has the potential to increase the efficiency and reliability of QCA.Figure 1.angioPy segmentationFigure 2.angioPy vs QCA (Medis)