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AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography
Kritika Iyer1, Cyrus P Najarian1, Aya A Fattah1
1University of Michigan, 500 S State St, Ann Arbor, MI, 48109, USA.
Insights
A new AI tool, AngioNet, automatically segments coronary vessels in X-ray angiography images. This offers precise measurements of stenosis severity, aiding in objective diagnosis and treatment planning for coronary artery disease (CAD).
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.
Abstract:
Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.
Related Concept Videos
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Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...