Deep Learning-Driven Architecture for Automated Segmentation and High-Risk Carotid Plaque Identification in High-Resolution MRI

Motivation: Carotid atherosclerosis significantly contributes to cardiovascular diseases, necessitating improved diagnostic methods for plaque characterization in HR-MRI. Goal(s): To develop a deep learning framework that automates the segmentation and classification of high-risk carotid plaques, enhancing diagnostic accuracy and risk assessment. Approach: Leveraging a two-stage deep learning model, the study employed a modified 3D U-net for accurate plaque segmentation and a ResNet-based architecture for classification. Advanced image augmentation techniques were applied to enhance data robustness before training. Results: Achieved a DSC of 0.8316, demonstrating high accuracy (89.74%) and AUC (0.9035) in differentiating symptomatic and asymptomatic plaques, confirming the model's efficacy in clinical diagnostics. Impact: This study significantly enhances the diagnostics of carotid atherosclerosis by leveraging high-resolution MRI and deep learning to accurately identify high-risk plaques, improving early intervention and potentially transforming outcomes in cardiovascular patient care.