Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jan 1:418:132598.
doi: 10.1016/j.ijcard.2024.132598. Epub 2024 Sep 26.

AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

Thabo Mahendiran  1 , Dorina Thanou  2 , Ortal Senouf  2 , Yassine Jamaa  3 , Stephane Fournier  4 , Bernard De Bruyne  5 , Emmanuel Abbé  2 , Olivier Muller  4 , Edward Andò  3
Affiliations
Free article

AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

Thabo Mahendiran et al. Int J Cardiol. .
Free article

Abstract

Background: Quantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g. angiography-derived fractional flow reserve). We developed AngioPy, a deep-learning model for coronary segmentation that employs user-defined ground-truth points to boost performance and minimise manual correction. We compared its performance without correction with an established QCA system.

Methods: Deep learning models integrating user-defined ground-truth points were developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. External validation was performed on a dataset of 580 images. Vessel dimensions from 203 images with mild/moderate stenoses segmented by AngioPy (without correction) and an established QCA system (Medis QFR®) were compared (609 diameters).

Results: The top-performing model had 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. Similar results were seen with external validation (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Vessel dimensions from AngioPy exhibited excellent agreement with QCA (r = 0.96 [95 % CI 0.95-0.96], p < 0.001; mean difference - 0.18 mm [limits of agreement (LOA): -0.84 to 0.49]), including the minimal luminal diameter (r = 0.93 [95 % CI 0.91-0.95], p < 0.001; mean difference - 0.06 mm [LOA: -0.70 to 0.59]).

Conclusion: AngioPy, an open-source tool, performs rapid and accurate coronary segmentation without the need for manual correction. It has the potential to increase the accuracy and efficiency of QCA.

Keywords: Artificial intelligence; Chronic coronary syndromes; Deep learning; Open source; Quantitative coronary angiography; Stenosis assessment.

PubMed Disclaimer

Conflict of interest statement

Declaration of competing interest BDB reports receiving consultancy fees from Boston Scientific and Abbott Vascular, research grants from Coroventis Research, Pie Medical Imaging, CathWorks, Boston Scientific, Siemens, HeartFlow Inc., and Abbott Vascular, and owning equity in Siemens, GE, Philips, HeartFlow Inc., Edwards Life Sciences, Bayer, Sanofi, Celyad. All the other authors have nothing to disclose. The following are the supplementary data related to this article. Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijcard.2024.132598.

LinkOut - more resources