지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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 UNet 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.
목차
등록된 정보가 없습니다.
참고문헌 (23)
참고문헌 신청
Honda O / 2004 / Echolucent carotid plaques predict future coronary events in patients with coronary artery disease / Journal of the American College of Cardiology 43(7):1177~1184
Kwon T / 2007 / Measurement of Carotid Intima-Media Thickness in ultrasound images by means of an automatic segmentation process based on machine learning / The Korean Society of Circulation 37:103~107
Liang Q / 2000 / A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images / IEEE Transactions Medical Imaging 19:127~142
Kim JS / 2016 / The significance of muscle echo intensity on ultrasound for focal neuropathy: The median- to ulnar-innervated muscle echo intensity ratio in carpal tunnel syndrome / Journal of Clinical Neurophysiology 127:880~885
Litjens G / 2017 / A survey on deep learning in medical image analysis / Medical Image Analysis 42:60~88
Kwon T / 2007 / Measurement of Carotid Intima-Media Thickness in ultrasound images by means of an automatic segmentation process based on machine learning / The Korean Society of Circulation 37:103~107
Liang Q / 2000 / A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images / IEEE Transactions Medical Imaging 19:127~142
Kim JS / 2016 / The significance of muscle echo intensity on ultrasound for focal neuropathy: The median- to ulnar-innervated muscle echo intensity ratio in carpal tunnel syndrome / Journal of Clinical Neurophysiology 127:880~885
Litjens G / 2017 / A survey on deep learning in medical image analysis / Medical Image Analysis 42:60~88
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