AGFA-Net: Attention-Guided and Feature-Aggregated Network for Coronary Artery Segmentation using Computed Tomography Angiography

TL;DR

This paper introduces AGFA-Net, a novel attention-guided deep learning model that significantly improves coronary artery segmentation accuracy from CCTA images, aiding diagnosis and treatment planning.

Contribution

The study presents a new 3D deep network with attention and feature aggregation modules, demonstrating superior performance over existing methods in coronary artery segmentation.

Findings

Achieved an average Dice coefficient of 86.74%

Reduced Hausdorff distance to 0.23 mm

Validated effectiveness through ablation studies

Abstract

Coronary artery disease (CAD) remains a prevalent cardiovascular condition, posing significant health risks worldwide. This pathology, characterized by plaque accumulation in coronary artery walls, leads to myocardial ischemia and various symptoms, including chest pain and shortness of breath. Accurate segmentation of coronary arteries from coronary computed tomography angiography (CCTA) images is crucial for diagnosis and treatment planning. Traditional segmentation methods face challenges in handling low-contrast images and complex anatomical structures. In this study, we propose an attention-guided, feature-aggregated 3D deep network (AGFA-Net) for coronary artery segmentation using CCTA images. AGFA-Net leverages attention mechanisms and feature refinement modules to capture salient features and enhance segmentation accuracy. Evaluation on a dataset comprising 1,000 CCTA scans demonstrates AGFA-Net's superior performance, achieving an average Dice coefficient similarity of 86.74% and a Hausdorff distance of 0.23 mm during 5-fold cross-validation. Ablation studies further validate the effectiveness of the proposed modules, highlighting their contributions to improved segmentation accuracy. Overall, AGFA-Net offers a robust and reliable solution for coronary artery segmentation, addressing challenges posed by varying vessel sizes, complex anatomies, and low image contrast.