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KMID : 1137820210420030100
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의공학회지
2021 Volume.42 No. 3 p.100 ~ p.106
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Development of Automatic Segmentation Algorithm of Intima-media Thickness of Carotid Artery in Portable Ultrasound Image Based on Deep Learning
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Choi Ja-Young
Kim Young-Jae
You Kyung-Min
Jang Albert Young-Woo
Chung Wook-Jin
Kim Kwang-Gi
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Abstract
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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 inter?vention, 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 com?plex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diag?nostic 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.
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KEYWORD
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IMT, Segmentation, U-Net, Attention U-Net, Pretrained U-Net, Preprocessing
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