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http://dx.doi.org/10.9717/kmms.2018.21.12.1407

Automated Ulna and Radius Segmentation model based on Deep Learning on DEXA  

Kim, Young Jae (Dept. of Biomedical Engineering, Gachon University College of Medicine)
Park, Sung Jin (Dept. of Biomedical Engineering, Gachon University College of Medicine)
Kim, Kyung Rae (Dept. of Biomedical Engineering, Gachon University College of HealthScience)
Kim, Kwang Gi (Dept. of Biomedical Engineering, Gachon University College of Medicine, Dept. of Biomedical Engineering, Gachon University College of HealthScience)
Publication Information
Journal of Korea Multimedia Society / v.21, no.12, 2018 , pp. 1407-1416 More about this Journal
Abstract
The purpose of this study was to train a model for the ulna and radius bone segmentation based on Convolutional Neural Networks and to verify the segmentation model. The data consisted of 840 training data, 210 tuning data, and 200 verification data. The learning model for the ulna and radius bone bwas based on U-Net (19 convolutional and 8 maximum pooling) and trained with 8 batch sizes, 0.0001 learning rate, and 200 epochs. As a result, the average sensitivity of the training data was 0.998, the specificity was 0.972, the accuracy was 0.979, and the Dice's similarity coefficient was 0.968. In the validation data, the average sensitivity was 0.961, specificity was 0.978, accuracy was 0.972, and Dice's similarity coefficient was 0.961. The performance of deep convolutional neural network based models for the segmentation was good for ulna and radius bone.
Keywords
DEXA; Deep Learning; Convolutional Neural Network; U-Net; Segmentation;
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