{"title":"LSMD: Long-Short Memory-Based Detection Network for Carotid Artery Detection in B-mode Ultrasound Video Streams.","authors":"Chunjie Shan, Yidan Zhang, Chunrui Liu, Zhibin Jin, Hanlin Cheng, Yidi Chen, Jing Yao, Shouhua Luo","doi":"10.1109/TUFFC.2024.3494019","DOIUrl":null,"url":null,"abstract":"<p><p>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 network (LSMD), 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 Multi-box Detector (SSD) algorithm as a baseline. The results show that the precision, recall, Average Precision at IoU = 0.50 (AP<sub>50</sub>), and mean Average Precision (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline (p < 0.001) respectively, while the model's inference latency reaches 6.97ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69ms 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.</p>","PeriodicalId":13322,"journal":{"name":"IEEE transactions on ultrasonics, ferroelectrics, and frequency control","volume":"PP ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on ultrasonics, ferroelectrics, and frequency control","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TUFFC.2024.3494019","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
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 network (LSMD), 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 Multi-box Detector (SSD) algorithm as a baseline. The results show that the precision, recall, Average Precision at IoU = 0.50 (AP50), and mean Average Precision (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline (p < 0.001) respectively, while the model's inference latency reaches 6.97ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69ms 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.
期刊介绍:
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control includes the theory, technology, materials, and applications relating to: (1) the generation, transmission, and detection of ultrasonic waves and related phenomena; (2) medical ultrasound, including hyperthermia, bioeffects, tissue characterization and imaging; (3) ferroelectric, piezoelectric, and piezomagnetic materials, including crystals, polycrystalline solids, films, polymers, and composites; (4) frequency control, timing and time distribution, including crystal oscillators and other means of classical frequency control, and atomic, molecular and laser frequency control standards. Areas of interest range from fundamental studies to the design and/or applications of devices and systems.