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SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model.

Ruoxuan Xu¹, Longhui Dai¹, Jianru Wang¹

¹School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China.

Journal of Imaging

|June 25, 2025

Summary

This summary is machine-generated.

Accurate coronary artery segmentation from CT angiography (CTA) is crucial for diagnosing coronary artery disease (CAD). The novel SADiff framework significantly improves segmentation accuracy, outperforming existing methods and showing great potential for CAD diagnosis and treatment.

Keywords:

coronary artery diffusion model image segmentation multi-scale spatial attention

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Area of Science:

Medical Imaging
Cardiovascular Disease Research
Artificial Intelligence in Healthcare

Background:

Coronary artery disease (CAD) is a leading global cause of death, necessitating precise diagnostic tools.
Accurate segmentation of coronary arteries in CT angiography (CTA) is vital for CAD diagnosis and treatment planning.
Existing segmentation methods struggle with challenges like small vessel size, morphological variations, low contrast, and motion artifacts.

Purpose of the Study:

To develop an advanced segmentation framework, SADiff, for improved coronary artery analysis from CTA images.
To address limitations of conventional and early deep learning methods in segmenting complex coronary artery structures.
To enhance the accuracy and generalizability of coronary artery segmentation for better CAD management.

Main Methods:

Proposed SADiff, a hybrid framework integrating a dilated attention network (DAN) for region extraction.
Incorporated a diffusion-based subnet for noise reduction in low-contrast areas.
Utilized a striped attention network (SAN) for refining tubular structures with morphological variations.

Main Results:

Achieved a Dice score of 83.48% and Hausdorff distance of 19.43 mm on the ImageCAS dataset, outperforming U-Net3D by 6.57% in Dice score.
Demonstrated generalizability with a Dice score of 79.42% on the private ImageLaPP dataset.
SADiff shows superior performance in segmenting coronary arteries compared to existing methods.

Conclusions:

SADiff offers a more efficient and versatile approach to coronary artery segmentation from CTA.
The framework shows significant potential for improving the accuracy of CAD diagnosis and treatment.
The study highlights the effectiveness of the hybrid attention and diffusion-based approach for complex medical image segmentation.