AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography

Area of Science:

• Medical Imaging
• Artificial Intelligence in Cardiology
• Computational Pathology

Background:

• Coronary Artery Disease (CAD) diagnosis relies on X-ray angiography to assess vessel narrowing (stenosis).
• Current visual estimation of stenosis by cardiologists can be subjective.
• A need exists for objective, quantitative methods for measuring diameter reduction in coronary vessels.

Purpose of the Study:

• To develop an automated method for segmenting coronary vessels in X-ray angiography images.
• To introduce AngioNet, a convolutional neural network designed for precise vessel segmentation.
• To improve the quantitative assessment of coronary stenosis severity.

Main Methods:

• Designed AngioNet, a convolutional neural network incorporating an Angiographic Processing Network (APN).
• The APN enables an end-to-end pipeline for image pre-processing and segmentation, learning optimal filters.
• Evaluated segmentation performance using metrics like Dice score, pixel accuracy, sensitivity, and specificity, with Deeplabv3+ backbone.

Main Results:

• AngioNet achieved a Dice score of 0.864, pixel accuracy of 0.983, sensitivity of 0.918, and specificity of 0.987 with the Deeplabv3+ backbone.
• The Angiographic Processing Network (APN) significantly enhanced segmentation performance.
• Demonstrated interchangeability with Quantitative Coronary Angiography for vessel diameter measurement.

Conclusions:

• AngioNet provides a powerful tool for automatic angiographic vessel segmentation.
• This automated approach can reduce subjectivity in stenosis assessment.
• Facilitates systematic anatomical assessment of coronary stenosis within clinical workflows.