Anatomy Guided Coronary Artery Segmentation from CCTA Using Spatial Frequency Joint Modeling

TL;DR

This paper introduces a novel 3D wavelet-based framework for coronary artery segmentation from CCTA images, integrating myocardial priors and multi-scale frequency modeling to improve accuracy and stability.

Contribution

It presents a new segmentation method combining spatial frequency joint modeling with anatomical priors and residual attention, outperforming existing models on public datasets.

Findings

Achieved a Dice coefficient of 0.8082

Outperformed several mainstream segmentation models

Confirmed the effectiveness of each component through ablation studies

Abstract

Accurate coronary artery segmentation from coronary computed tomography angiography is essential for quantitative coronary analysis and clinical decision support. Nevertheless, reliable segmentation remains challenging because of small vessel calibers, complex branching, blurred boundaries, and myocardial interference. We propose a coronary artery segmentation framework that integrates myocardial anatomical priors, structure aware feature encoding, and three dimensional wavelet inverse wavelet transformations. Myocardial priors and residual attention based feature enhancement are incorporated during encoding to strengthen coronary structure representation. Wavelet inverse wavelet based downsampling and upsampling enable joint spatial frequency modeling and preserve multi scale structural consistency, while a multi scale feature fusion module integrates semantic and geometric information in the decoding stage. The model is trained and evaluated on the public ImageCAS dataset using a 3D overlapping patch based strategy with a 7:1:2 split for training, validation, and testing. Experimental results demonstrate that the proposed method achieves a Dice coefficient of 0.8082, Sensitivity of 0.7946, Precision of 0.8471, and an HD95 of 9.77 mm, outperforming several mainstream segmentation models. Ablation studies further confirm the complementary contributions of individual components. The proposed method enables more stable and consistent coronary artery segmentation under complex geometric conditions, providing reliable segmentation results for subsequent coronary structure analysis tasks.