Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
Coronary artery disease (CAD) remains a leading cause of mortality worldwide,
requiring accurate segmentation and stenosis detection using Coronary Computed
Tomography angiography (CCTA). Existing methods struggle with challenges such
as low contrast, morphological variability and small vessel segmentation. To
address these limitations, we propose the Myocardial Region-guided Feature
Aggregation Net, a novel U-shaped dual-encoder architecture that integrates
anatomical prior knowledge to enhance robustness in coronary artery
segmentation. Our framework incorporates three key innovations: (1) a
Myocardial Region-guided Module that directs attention to coronary regions via
myocardial contour expansion and multi-scale feature fusion, (2) a Residual
Feature Extraction Encoding Module that combines parallel spatial channel
attention with residual blocks to enhance local-global feature discrimination,
and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of
hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies
prediction uncertainty, supporting clinical interpretability. For stenosis
detection, a morphology-based centerline extraction algorithm separates the
vascular tree into anatomical branches, enabling cross-sectional area
quantification and stenosis grading. The superiority of MGFA-Net was
demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an
HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for
stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis
pipeline provides automated, clinically interpretable CAD assessment, bridging
deep learning with anatomical prior knowledge for precision medicine. Our code
is publicly available at http://github.com/chenzhao2023/MGFA_CCTA
