Multi-stage deep learning architecture for carotid artery segmentation and stenosis evaluation: comparative study with digital subtraction angiography

BACKGROUND: High-resolution magnetic resonance imaging (HR-MRI) provides a non-invasive, radiation-free approach for evaluating stenosis caused by carotid atherosclerosis. However, manual recognition is time-consuming and inter-observer variability. We propose a novel architecture for automated segmentation and stenosis evaluation of extracranial carotid arteries by HR-MRI in comparison with digital subtraction angiography (DSA). METHODS: The 641 stenotic arteries from 422 patients retrospectively collected from three tertiary hospitals were divided into a training-validation set (372 patients, 545 lesions) and an independent test set (50 patients, 96 lesions). An external validation set (89 patients, 168 lesions) was collected from the fourth tertiary hospital. RESULTS: The architecture demonstrated high consistency with manual segmentation and DSA diagnostic criteria, with mean Dice similarity coefficients of 0.97 ± 0.01, 0.96 ± 0.01, and stenosis evaluation accuracies of 0.88, 0.86 on the independent test and external validation set, respectively. CONCLUSION: Thus, the proposed architecture achieved accuracy comparable to manual segmentation by physicians and demonstrated high consistency with DSA diagnostic criteria. By shortening diagnostic time and minimizing inter-observer variability, the proposed architecture is promising to offer a reliable, efficient, and intelligent tool for diagnosing head and neck atherosclerotic disease and assessing stroke risk.