딥러닝 모델을 이용한 휴대용 무선 초음파 영상에서의 경동맥 내중막 두께 자동 분할 알고리즘 개발
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최자영 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 유경민 | - |
dc.contributor.author | 장영우 | - |
dc.contributor.author | 정욱진 | - |
dc.contributor.author | 김광기 | - |
dc.date.accessioned | 2021-07-01T08:41:31Z | - |
dc.date.available | 2021-07-01T08:41:31Z | - |
dc.date.created | 2021-07-01 | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 1229-0807 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81419 | - |
dc.description.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. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한의용생체공학회 | - |
dc.relation.isPartOf | 의공학회지 | - |
dc.title | 딥러닝 모델을 이용한 휴대용 무선 초음파 영상에서의 경동맥 내중막 두께 자동 분할 알고리즘 개발 | - |
dc.title.alternative | Development of Automatic Segmentation Algorithm of Intima-media Thickness of Carotid Artery in Portable Ultrasound Image Based on Deep Learning | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.bibliographicCitation | 의공학회지, v.42, no.3, pp.100 - 106 | - |
dc.identifier.kciid | ART002731224 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 106 | - |
dc.citation.startPage | 100 | - |
dc.citation.title | 의공학회지 | - |
dc.citation.volume | 42 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | 최자영 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 유경민 | - |
dc.contributor.affiliatedAuthor | 장영우 | - |
dc.contributor.affiliatedAuthor | 정욱진 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | IMT | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | U-Net | - |
dc.subject.keywordAuthor | Attention U-Net | - |
dc.subject.keywordAuthor | Pretrained U-Net | - |
dc.subject.keywordAuthor | Preprocessing | - |
dc.description.journalRegisteredClass | kci | - |
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