Development and evaluation of deep learning models for automatic coronary stenosis segmentation in X-ray angiography

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Disease

Background:

• Accurate segmentation of coronary artery stenosis in X-ray angiography (XRA) is vital for assessing disease severity and guiding treatment.
• Current methods rely on subjective visual evaluation, leading to significant inter-observer variability.

Purpose of the Study:

• To develop a deep learning model with a novel Hybrid Context-Aware Attention (HCA) module for enhanced stenosis segmentation in XRA.
• To improve the accuracy and anatomical consistency of stenosis segmentation compared to existing methods.

Main Methods:

• A deep learning model incorporating a novel Hybrid Context-Aware Attention (HCA) module was proposed.
• The HCA module features a parallel dual-pathway design integrating global inter-channel attention and grouped multi-scale spatial aggregation.
• The model was evaluated on three independent datasets.

Main Results:

• The proposed model achieved competitive and leading performance across multiple metrics compared to existing approaches.
• Ablation studies and attention visualization confirmed the module's effectiveness in reducing segmentation errors.
• The model demonstrated enhanced focus on stenotic regions.

Conclusions:

• The developed deep learning model with the HCA module offers an effective and generalizable solution for stenosis segmentation in XRA.
• This approach has the potential to support standardized, objective assessment in clinical practice for coronary artery disease.