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

Accurate segmentation of stenosis in X-ray angiography (XRA) images is crucial for the objective assessment of stenosis severity and subsequent treatment planning in coronary artery disease. Current clinical practice primarily relies on subjective visual evaluation, which suffers from significant inter-observer variability. In this work, we propose a deep learning model enhanced with a novel Hybrid Context-Aware Attention (HCA) module. HCA employs a parallel dual-pathway design that integrates global inter-channel attention and grouped multi-scale spatial aggregation. This integration enhances feature discriminability and spatial-context modeling, leading to more accurate and anatomically consistent stenosis segmentation in XRA. Evaluated on three independent datasets, our method achieves competitive performance against existing approaches across multiple metrics, demonstrating consistent leading performance. Ablation and attention visualization studies further confirm the contribution of the designed module to reducing segmentation errors and enhancing focus on stenotic regions. These findings demonstrate that the proposed model is an effective and generalizable approach for stenosis segmentation in XRA, with the potential to support standardized assessment in clinical practice.