In carotid arteries, plaque can develop as localized elevated lesions. The Jellyfish sign, marked by fluctuating plaque surfaces with blood flow pulsation, is a dynamic characteristic of these plaques that has recently attracted attention. Detecting this sign is vital, as it is often associated with cerebral infarction. This paper proposes an ultrasound video-based classification method for the Jellyfish sign, using deep neural networks. The proposed method first preprocesses carotid ultrasound videos to separate the movement of the vascular wall from plaque movements. These preprocessed videos are then combined with plaque surface information and fed into a deep learning model comprising convolutional and recurrent neural networks, enabling the efficient classification of the Jellyfish sign. The proposed method was verified using ultrasound video images from 200 patients. Ablation studies demonstrated the effectiveness of each component of the proposed method.

翻译：颈动脉中斑块可形成局部隆起性病变。水母征表现为斑块表面随血流搏动而波动，是这些斑块的一种动态特征，近年来备受关注。检测该征象至关重要，因其常与脑梗死相关。本文提出一种基于超声视频的水母征深度神经网络分类方法。所提方法首先对颈动脉超声视频进行预处理，以分离血管壁运动与斑块运动。随后将这些预处理视频与斑块表面信息融合，输入由卷积神经网络和循环神经网络构成的深度学习模型，实现对水母征的高效分类。该方法使用200例患者的超声视频图像进行了验证。消融研究证明了所提方法各组成部分的有效性。