Comput Methods Programs Biomed. 2025 Sep 9;272:109072. doi: 10.1016/j.cmpb.2025.109072. Online ahead of print.
ABSTRACT
BACKGROUND AND OBJECTIVE: Coronary Artery Disease (CAD) diagnosis relies heavily on coronary angiography, yet interpretation suffers from variability. Deep learning (DL) offers potential for improvement, particularly in vessel segmentation, a critical step for analysis. This study aims to enhance vessel segmentation accuracy in angiography using a DL framework incorporating advanced preprocessing and texture features.
METHODS: We developed a U-Net architecture integrating Texture Representation of Image (TRI) features (Haralick and Law features) to capture subtle vascular details. Advanced preprocessing (Laplacian Pyramid Restoration, Gaussian Differential Scale-Invariance) was applied to improve image quality. The model was pre-trained on the DRIVE dataset and fine-tuned using 7600 clinical angiography images. Performance was evaluated on a held-out test set (19 patients, ∼1700 images) from the same institution and benchmarked against the public ARCADE dataset. Statistical tests assessed performance improvements. Post-segmentation analysis included branching point detection and vessel diameter visualization using heatmaps.
RESULTS: The proposed method achieved high segmentation performance on the clinical test set (Accuracy: 0.98, Precision: 0.87, Sensitivity: 0.91, F1-score: 0.89, IoU: 0.801, with CIs provided). Ablation studies confirmed statistically significant contributions from both preprocessing and TRI features (p < 0.01 for all metrics). Performance on the ARCADE benchmark was also strong (F1-score: 0.78), considering annotation differences.
CONCLUSIONS: Integrating TRI features and advanced preprocessing with a U-Net architecture significantly improves coronary angiography vessel segmentation. This provides a robust foundation for subsequent quantitative analysis potentially supporting CAD assessment. While limitations exist regarding external validation and direct clinical impact assessment, the enhanced segmentation capability represents a valuable advancement for angiographic image analysis tools.
PMID:40983000 | DOI:10.1016/j.cmpb.2025.109072
