Mask R-CNN-based carotid plaque localization in ultrasound images

In this study, we present the refinement of a Mask R-CNN model initially designed for carotid lumen detection to automatically generate bounding boxes (BB) enclosing atherosclerotic plaque. Although the model also produces segmentation masks within these bounding boxes, this study primarily evaluates bounding box detection performance as the key step to support segmentation in our ultrasound elastography workflow.

We utilize a PyTorch torchvision implementation of Mask R-CNN for carotid plaque detection and BB placement. Our dataset consists of 118 severe stenotic carotid plaques from patients clinically indicated for carotid endarterectomy. Due to variability plaque presentation, different R-CNN models showed varying results based on the allowed number of prediction regions. An overview analysis of shared predictions from these models showed slight improvement over individual model results.

Using bounding box detection as the primary endpoint, we achieved a maximum Dice similarity coefficient (DSC) of 0.74 and intersection over union (IoU) of 0.61 for the best-performing model, with a filtered multimodel approach improving DSC to 0.76. The corresponding plaque mask segmentation performance was lower ( $\mathrm{DSC}=0.64$ , $\mathrm{IoU}=0.49$ ), reflecting the increased difficulty of precise plaque delineation.

Due to significant variation in plaque presentation and types among patients, the accuracy of the Plaque Mask R-CNN network would benefit from incorporating additional patient datasets to increase variation in the training data.