손목 관절 단순 방사선 영상에서 딥 러닝을 이용한 전후방 및 측면 영상 분류와 요골 영역 분할Classification of Anteroposterior/Lateral Images and Segmentation of the Radius Using Deep Learning in Wrist X-rays Images
- Other Titles
- Classification of Anteroposterior/Lateral Images and Segmentation of the Radius Using Deep Learning in Wrist X-rays Images
- Authors
- 이기표; 김영재; 이상림; 김광기
- Issue Date
- Apr-2020
- Publisher
- 대한의용생체공학회
- Keywords
- Distal radius fractures; Deep learning; Classification; Segmentation; X-rays
- Citation
- 의공학회지, v.41, no.2, pp.94 - 100
- Journal Title
- 의공학회지
- Volume
- 41
- Number
- 2
- Start Page
- 94
- End Page
- 100
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/41907
- ISSN
- 1229-0807
- 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.
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