Research on the IVUS Border Detection Method Based on Improved TransUnet
Wang Yuanyuan1&, Dong Fang1&, Shang Lina1, Zhang Cui1, Xia Shunren1,2#*
1(School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310000, China) 2(Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310000, China)
Abstract：Intravascular ultrasound (IVUS) is a popular imaging technique that can observe the internal structure of blood vessels. The extraction of lumen and media-adventitia border in the IVUS image is vitally important for the accurate diagnosis of coronary atherosclerosis. To solve the problems of complex structure, low contrast and difficult boundary detection in the IVUS images, a modified TransUnet network was proposed in this paper to achieve pixel-wise classification results. Firstly, to overcome the difficulty of IVUS image border detection, data augmentation strategy is performed based on four kinds of image structure models, including edge vessel, vessel bifurcation, guide wire artifact and shadow. Then, a position named Polar-bias was proposed in the TransUnet network. The Polar-bias combines the ring structure distribution characteristics of IVUS images. The modified TransUnet network was applied to classify the IVUS images in pixel-wise level. Finally, the classified results were employed to optimize the external force field of GVF Snake model, which was used to detect the lumen and media-adventitia border in IVUS images. Two public datasets with different ultrasonic imaging center frequencies (a total of 512 images) were used for testing and verification, and three evaluation indexes of JMard measure (JM), Hausdorff distance (HD) and percentage of area difference (PAD) were introduced. The proposed method has achieved JM (0.87), HD (0.87) and PAD (0.18) in dataset A, and JM(0.91), HD(0.25) and PAD(0.08) in dataset B. The experimental results demonstrated that the proposed method outperformed other state-of-the-art ones in the border detection of two datasets.
王媛媛, 董芳, 尚丽娜, 张翠, 夏顺仁. 基于改进TransUnet网络的血管内超声图像边界提取方法研究[J]. 中国生物医学工程学报, 2023, 42(1): 41-50.
Wang Yuanyuan, Dong Fang, Shang Lina, Zhang Cui, Xia Shunren. Research on the IVUS Border Detection Method Based on Improved TransUnet. Chinese Journal of Biomedical Engineering, 2023, 42(1): 41-50.
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