Deep vessel segmentation with U-Net and texture representation of image (TRI) features provides a foundation for improved objective and automated analysis of coronary artery disease from angiography

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.