AngioNet

AngioNet performs automatic segmentation of coronary vessels in X-ray angiography images to quantify vessel diameter and support objective assessment of Coronary Artery Disease (CAD).

Key Features:

• Automatic Vessel Segmentation: A convolutional neural network automates segmentation of coronary arteries in X-ray angiography images for precise vessel delineation.
• Angiographic Processing Network (APN): APN learns optimal pre-processing filters to enhance image quality and improve segmentation performance across different network backbones.
• Deeplabv3+ Backbone and Performance Metrics: Implemented with a Deeplabv3+ backbone, AngioNet achieves a Dice score of 0.864, pixel accuracy of 0.983, sensitivity of 0.918, and specificity of 0.987.
• End-to-End Pre-processing and Segmentation: The APN enables an end-to-end pipeline that applies learned pre-processing filters followed by segmentation.
• Quantitative Diameter Measurement: Provides quantitative measurements of vessel diameter and diameter reduction for assessment of stenosis.
• Interchangeability with QCA: Diameter measurements have been demonstrated to be interchangeable with Quantitative Coronary Angiography (QCA).

Scientific Applications:

• Assessment of Coronary Stenosis: Systematic anatomical assessment of coronary stenosis from X-ray angiography images.
• Objective CAD Evaluation: Objective quantification of vessel narrowing to support diagnosis and severity grading of Coronary Artery Disease.
• Therapeutic Decision Support: Quantitative diameter reduction measurements to inform interventional decisions such as stent placement.

Methodology:

Convolutional neural network architecture using a Deeplabv3+ backbone enhanced by the Angiographic Processing Network (APN), which learns optimal pre-processing filters and implements an end-to-end pipeline for image pre-processing and segmentation.