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

Chunjie Shan, Yidan Zhang, Chunrui Liu, Zhibin Jin, Hanlin Cheng, Yidi Chen, Jing Yao, Shouhua Luo
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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.

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LSMD:基于长短记忆的检测网络,用于 B 型超声视频流中的颈动脉检测。
颈动脉粥样硬化斑块是 II 型糖尿病的主要并发症,颈动脉超声通常用于诊断颈动脉血管疾病。在基层医院,经验较少的超声医生往往难以持续捕捉标准颈动脉图像并识别斑块。为解决这一问题,我们提出了一种新方法--基于长短记忆的检测网络(LSMD),用于超声视频流中的颈动脉检测,促进关键解剖结构和斑块的识别和定位。这种方法通过短期时空聚合(STA)和长期时空聚合(LTA)模块对短距离和长距离时空特征进行建模,以最小的延迟有效扩展时空感受野,提高颈动脉解剖结构和斑块的检测效率。具体来说,我们引入了具有动态更新策略的内存缓冲区,以确保广泛的时间感受野覆盖,同时最大限度地降低内存和计算成本。我们在 80 个颈动脉超声视频上对所提出的模型进行了训练,并在 50 个视频上进行了评估,所有视频都由医生对颈动脉解剖结构和斑块进行了注释。以基于单帧图像的单枪多箱检测器(SSD)算法为基准,对训练好的 LSMD 在验证集和测试集上的性能进行了评估。结果显示,精确度、召回率、IoU = 0.50时的平均精确度(AP50)和平均平均精确度(mAP)分别比基线高出6.83%、12.29%、11.23%和13.21%(p < 0.001),而模型的推理延迟在桌面级GPU(英伟达RTX 3090Ti)上为6.97ms,在边缘计算设备(Jetson Orin Nano)上为29.69ms。这些研究结果表明,LSMD 可以通过实时推理准确定位颈动脉解剖结构和斑块,显示了其在临床实践中提高诊断准确性的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.70
自引率
16.70%
发文量
583
审稿时长
4.5 months
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.
期刊最新文献
TinyProbe: A Wearable 32-channel Multi-Modal Wireless Ultrasound Probe. LSMD: Long-Short Memory-Based Detection Network for Carotid Artery Detection in B-mode Ultrasound Video Streams. A Phantom-Free Approach for Estimating the Backscatter Coefficient of Aggregated Red Blood Cells applied to COVID-19 Patients. High-frequency wearable ultrasound array belt for small animal echocardiography. Deep Power-aware Tunable Weighting for Ultrasound Microvascular Imaging.
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