利用斑块表面边缘结合卷积与循环神经网络对具有水母征的颈动脉斑块进行分类
Classification of Carotid Plaque with Jellyfish Sign Through Convolutional and Recurrent Neural Networks Utilizing Plaque Surface Edges
AI总结：
本文提出一种基于超声视频的深度学习方法，结合卷积与循环神经网络及斑块表面信息，高效分类与脑梗死相关的颈动脉斑块水母征，并在200名患者数据上验证了有效性。
摘要：
在颈动脉中，斑块可发展为局灶性隆起病变。水母征以斑块表面随血流搏动而波动为特征，是这些斑块的一种动态特征，近期引起了关注。检测该征象至关重要，因其常与脑梗死相关。本文提出了一种基于超声视频的水母征分类方法，使用深度神经网络。该方法首先对颈动脉超声视频进行预处理，将血管壁的运动与斑块运动分离。随后将这些预处理后的视频与斑块表面信息相结合，输入由卷积神经网络和循环神经网络组成的深度学习模型，从而实现对水母征的高效分类。所提出的方法使用来自200名患者的超声视频图像进行了验证。消融实验证明了该方法各组件的有效性。
英文摘要：
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.
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