Carotid Artery Plague Segmentation Model Based on Dual-Modal

Abstract

Ultrasonography (US) and contrast-enhanced ultrasound (CEUS) are effective imaging tools for analyzing the spatial and temporal characteristics of lesions and diagnosing or predicting diseases. At the same time, US is characterized by blurred boundaries and strong noise interference. Therefore, evaluating plaques and depicting lesions frame-by-frame is a time-consuming task, which poses a challenge in analyzing US videos using deep learning techniques. However, despite the existing methods for US and CEUS image segmentation, there are still limited approaches capable of integrating the feature information from these two distinct image types. Furthermore, these methods require additional optimization to enhance their capacity for extracting comprehensive global contextual information. To address the problem, we propose a U-shaped structured network model based on Transformer in this paper. The network is composed of two parts, that is, the dual-modal information interaction fusion module and the enhanced feature extraction module. The first module is used to extract comprehensive US and CEUS features and fuse them at multiple scales. The second module is used to enhance feature extraction capabilities. This network enables precise localization of the lesion and clear depiction of the region of interest in US. Our model achieved a Dice of 91.62% and an IoU of 88.04% on the carotid plaque segmentation dataset. The experimental results show that the performance of our designed network on the carotid artery dataset is better than that of the SOTA models.