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http://dx.doi.org/10.9718/JBER.2021.42.3.100

Development of Automatic Segmentation Algorithm of Intima-media Thickness of Carotid Artery in Portable Ultrasound Image Based on Deep Learning  

Choi, Ja-Young (Department of Biomedical Engineering, College of Health Science, Gachon University)
Kim, Young Jae (Department of Biomedical Engineering, College of Medicine, Gachon University)
You, Kyung Min (Gachon Cardiovascular Research Institute, Gachon University)
Jang, Albert Youngwoo (Gachon Cardiovascular Research Institute, Gachon University)
Chung, Wook-Jin (Gachon Cardiovascular Research Institute, Gachon University)
Kim, Kwang Gi (Department of Biomedical Engineering, College of Health Science, Gachon University)
Publication Information
Journal of Biomedical Engineering Research / v.42, no.3, 2021 , pp. 100-106 More about this Journal
Abstract
Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.
Keywords
IMT; Segmentation; U-Net; Attention U-Net; Pretrained U-Net; Preprocessing;
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