A Graph Neural Network-Based Framework for Reliability Prediction of Automated Carotid Intima-Media Thickness Segmentation

Abstract

Cardiovascular diseases remain the leading cause of global mortality. Carotid intima-media thickness (CIMT) is a well-established surrogate biomarker for early atherosclerosis detection. Manual CIMT ultrasound segmentations are more time-consuming and prone to error than automated CIMT ultrasound segmentations. This study presents a two-stage framework that integrates metric-based evaluation with Graph Neural Network (GNN)-based learning for automated CIMT segmentation evaluation and reliability prediction. In the first stage, segmentation performance was evaluated using overlap-based metrics, including the Dice Similarity Coefficient (DSC) and Jaccard Index (JI), together with the distance- based Point-to-Distance Metric (PDM). In the second stage, a GNN was trained on a patient similarity graph constructed from aggregated clinical features and segmentation-derived metrics for patient-level segmentation reliability prediction. The metric-based evaluation yielded a mean DSC of 0.915 ± 0.143, a mean JI of 0.863 ± 0.158, and PDM values of 0.239 ± 0.070 mm and 0.236 ± 0.067 mm for the Lumen– Intima and Media–Adventitia boundaries, respectively, indicating close agreement between automated and manual segmentations. The GNN-based learning achieved a Pearson’s correlation coefficient (r) of 0.972 with a Mean Absolute Error (MAE) of 0.017 for mean DSC and an r of 0.946 with an MAE of 0.015 for mean JI, indicating high consistency between predicted and true values. The proposed framework reduces reliance on manual segmentations by demonstrating robust performance of automated segmentations and enabling patient-level segmentation reliability prediction