CPTV: Classification by tracking of carotid plaque in ultrasound videos. (March 2023)

• Record Type: Journal Article
• Authors: Xie, Jiang; Li, Ying; Xu, Xiaochun; Wei, Jinzhu; Li, Haozhe; Wu, Shuo; Chen, Haibing
• Is Part Of: Computerized medical imaging and graphics. Volume 104(2023)
• Publication Date: 2023-03
• Subjects: Convolutional Neural Network -- Carotid Plaque -- Computer-aided Diagnosis -- Ultrasound Videos -- Classification by Tracking; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102175
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: The risk assessment of carotid plaque is strongly related to the plaque echo status in ultrasound. However, the echo classification of carotid plaques based on ultrasound remains challenging due to the changes in plaque shape and semantics, along with the complex vascular environment. This study proposed a framework for Classification of Plaque by Tracking Videos (CPTV). To the best of our knowledge, this is the first study on plaque classification by tracking ultrasound video rather than a sonographic view, which achieves accurate localization and stable echo classification. In the tracking task, Multi-scale Decoupling Tracking (MDTrack) module including Multi-scale Dilated Encoder (MDE) and Internal-Exterior Feature Decoupling (IEFD) was proposed to solve the problems caused by shape and semantic variations to achieve accurate plaque localization in ultrasound. In the classification task, the Tracking-assisted 3D Attention (T3D-Attention) module included recombination and 3D-Attention extracted plaque features and echo-related features in the vascular environment. The experiments demonstrated that the performance of CPTV is better than current mainstream tracking and classification methods, indicating that the tracking-assistance classification is a kind of enhancement method with high universality and stability in the plaque in ultrasound. Highlights: Echo classification of plaques in ultrasound videos is provided. This is the first study to deeply combine plaque tracking and classification. This framework is robust in identifying plaques with inconsistent echo in video.