LSMD: Long-Short Memory-Based Detection Network for Carotid Artery Detection in B-Mode Ultrasound Video Streams

Carotid atherosclerotic plaques are a major complication associated with type II diabetes, and carotid ultrasound is commonly used for diagnosing carotid vascular disease. In primary hospitals, less experienced ultrasound physicians often struggle to consistently capture standard carotid images and identify plaques. To address this issue, we propose a novel approach, the long-short memory-based detection (LSMD) network, for carotid artery detection in ultrasound video streams, facilitating the identification and localization of critical anatomical structures and plaques. This approach models short- and long-distance spatiotemporal features through short-term temporal aggregation (STA) and long-term temporal aggregation (LTA) modules, effectively expanding the temporal receptive field with minimal delay and enhancing the detection efficiency of carotid anatomy and plaques. Specifically, we introduce memory buffers with a dynamic updating strategy to ensure extensive temporal receptive field coverage while minimizing memory and computation costs. The proposed model was trained on 80 carotid ultrasound videos and evaluated on 50, with all videos annotated by physicians for carotid anatomies and plaques. The trained LSMD was evaluated for performance on the validation and test sets using the single-frame image-based single shot multibox detector (SSD) algorithm as a baseline. The results show that the precision, recall, average precision (AP) at ( ), and mean AP (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline ( ), respectively, while the model's inference latency reaches 6.97 ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69 ms on an edge computing device (Jetson Orin Nano). These findings demonstrate that LSMD can accurately localize carotid anatomy and plaques with real-time inference, indicating its potential for enhancing diagnostic accuracy in clinical practice.