손목 관절 단순 방사선 영상에서 딥 러닝을 이용한 전후방 및 측면 영상 분류와 요골 영역 분할
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이기표 | - |
dc.contributor.author | 김영재 | - |
dc.contributor.author | 이상림 | - |
dc.contributor.author | 김광기 | - |
dc.date.available | 2020-05-12T11:41:51Z | - |
dc.date.created | 2020-05-06 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 1229-0807 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/41907 | - |
dc.description.abstract | The purpose of this study was to present the models for classifying the wrist X-ray images by types and for segmenting the radius automatically in each image using deep learning and to verify the learned models. The data were a total of 904 wrist X-rays with the distal radius fracture, consisting of 472 anteroposterior (AP) and 432 lateral images. The learning model was the ResNet50 model for AP/lateral image classification, and the U-Net model for segmentation of the radius. In the model for AP/lateral image classification, 100.0% was showed in precision, recall, and F1 score and area under curve (AUC) was 1.0. The model for segmentation of the radius showed an accuracy of 99.46%, a sensitivity of 89.68%, a specificity of 99.72%, and a Dice similarity coefficient of 90.05% in AP images and an accuracy of 99.37%, a sensitivity of 88.65%, a specificity of 99.69%, and a Dice similarity coefficient of 86.05% in lateral images. The model for AP/lateral classification and the segmentation model of the radius learned through deep learning showed favorable performances to expect clinical application. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한의용생체공학회 | - |
dc.relation.isPartOf | 의공학회지 | - |
dc.title | 손목 관절 단순 방사선 영상에서 딥 러닝을 이용한 전후방 및 측면 영상 분류와 요골 영역 분할 | - |
dc.title.alternative | Classification of Anteroposterior/Lateral Images and Segmentation of the Radius Using Deep Learning in Wrist X-rays Images | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.bibliographicCitation | 의공학회지, v.41, no.2, pp.94 - 100 | - |
dc.identifier.kciid | ART002582189 | - |
dc.description.isOpenAccess | N | - |
dc.citation.endPage | 100 | - |
dc.citation.startPage | 94 | - |
dc.citation.title | 의공학회지 | - |
dc.citation.volume | 41 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | 이기표 | - |
dc.contributor.affiliatedAuthor | 김영재 | - |
dc.contributor.affiliatedAuthor | 김광기 | - |
dc.subject.keywordAuthor | Distal radius fractures | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | X-rays | - |
dc.description.journalRegisteredClass | kci | - |
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