Real-Time Online Intelligent Segmentation of Carotid Artery Ultrasound Video

The morphology and structure of carotid arteries can provide valuable information about the degree of arterial hardening and plaque formation. This information holds significant clinical importance in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. Ultrasound image segmentation technology precisely extracts carotid artery contours from background images, providing physicians with anatomical and pathological insights. Traditional carotid artery ultrasound image segmentation methods rely on offline volume data, posing issues related to low efficiency, poor accuracy, and dependence on physician expertise during clinical use. This study introduces a real-time online intelligent segmentation approach for carotid artery ultrasound, aimed at enhancing clinical physicians’ operational efficiency and diagnostic accuracy during ultrasound examinations. Utilizing space-time memory, we propose an end-to-end video segmentation network that simplifies panoramic segmentation into localized segmentation through coordinate regression, providing relative spatial position information. After processing and training on 60 expert-annotated ultrasound volume data samples, the model achieved an average Dice of 90.51% on the carotid artery ultrasound image test set. The network processed images at a speed of 40 frames per second, achieving the goal of real-time online intelligent and precise segmentation of carotid artery ultrasound images.