Segmentation of coronary artery and calcification using prior knowledge based deep learning framework

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Research

Background:

• Computed tomography angiography (CTA) is crucial for screening coronary artery calcification.
• Manual screening of CTA is time-consuming due to complex coronary artery anatomy.
• Existing deep learning segmentation methods often lack anatomical prior knowledge, leading to inaccuracies.

Purpose of the Study:

• Develop a deep learning framework for accurate coronary artery and calcification segmentation using anatomical priors.
• Investigate the predictive capability of the coronary artery and calcification volume ratio for rotational atherectomy (RA).

Main Methods:

• A novel segmentation framework integrating variational autoencoder-based centerline extraction, self-attention, and logic operations.
• Utilizing 3D CTA patches and refining features based on spatial relationships between lumen and calcification.
• Generating segmentation results for coronary artery and calcification.

Main Results:

• The proposed framework significantly outperforms state-of-the-art methods on a CTA dataset.
• Ablation studies confirm the positive impact of individual modules on segmentation performance.
• The volume ratio of segmented coronary artery and calcification achieved 0.75 prediction accuracy for RA.

Conclusions:

• Incorporating anatomical prior knowledge enhances deep learning-based segmentation of coronary arteries and calcifications.
• The volume ratio of segmented coronary artery and calcification serves as a valuable predictor for RA.