딥러닝 모델을 이용한 휴대용 무선 초음파 영상에서의 경동맥 내중막 두께 자동 분할 알고리즘 개발Development of Automatic Segmentation Algorithm of Intima-media Thickness of Carotid Artery in Portable Ultrasound Image Based on Deep Learning
- Other Titles
- Development of Automatic Segmentation Algorithm of Intima-media Thickness of Carotid Artery in Portable Ultrasound Image Based on Deep Learning
- Authors
- 최자영; 김영재; 유경민; 장영우; 정욱진; 김광기
- Issue Date
- Jun-2021
- Publisher
- 대한의용생체공학회
- Keywords
- IMT; Segmentation; U-Net; Attention U-Net; Pretrained U-Net; Preprocessing
- Citation
- 의공학회지, v.42, no.3, pp.100 - 106
- Journal Title
- 의공학회지
- Volume
- 42
- Number
- 3
- Start Page
- 100
- End Page
- 106
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81419
- ISSN
- 1229-0807
- 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 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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Collections - 의과대학 > 의학과 > 1. Journal Articles
- 보건과학대학 > 의용생체공학과 > 1. Journal Articles
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