Classification of Carotid Plaque with Jellyfish Sign Through Convolutional and Recurrent Neural Networks Utilizing Plaque Surface Edges
This work addresses a domain-specific medical imaging problem for detecting plaque characteristics associated with cerebral infarction, presenting an incremental improvement in classification methods.
The paper tackled the problem of classifying the Jellyfish sign in carotid plaque using ultrasound videos, proposing a deep learning method that achieved verification on data from 200 patients with ablation studies confirming component effectiveness.
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.