Classification of Carotid Plaque with Jellyfish Sign Through Convolutional and Recurrent Neural Networks Utilizing Plaque Surface Edges
CoRR(2024)
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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