Refined Feature-based Multi-frame and Multi-scale Fusing Gate network for accurate segmentation of plaques in ultrasound videos

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.

Ultrasound Image Analysis with Vision Transformers-Review

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.