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http://dx.doi.org/10.9717/kmms.2019.22.12.1376

Multiple Inputs Deep Neural Networks for Bone Age Estimation Using Whole-Body Bone Scintigraphy  

Nguyen, Phap Do Cong (Department of Electronics and Computer Engineering, Chonnam National University)
Baek, Eu-Tteum (Department of Electronics and Computer Engineering, Chonnam National University)
Yang, Hyung-Jeong (Department of Electronics and Computer Engineering, Chonnam National University)
Kim, Soo-Hyung (Department of Electronics and Computer Engineering, Chonnam National University)
Kang, Sae-Ryung (Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital)
Min, Jung-Joon (Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School)
Publication Information
Journal of Korea Multimedia Society / v.22, no.12, 2019 , pp. 1376-1384 More about this Journal
Abstract
The cosmetic and behavioral aspects of aging have become increasingly evident over the years. Physical aging in people can easily be observed on their face, posture, voice, and gait. In contrast, bone aging only becomes apparent once significant bone degeneration manifests through degenerative bone diseases. Therefore, a more accurate and timely assessment of bone aging is needed so that the determinants and its mechanisms can be more effectively identified and ultimately optimized. This study proposed a deep learning approach to assess the bone age of an adult using whole-body bone scintigraphy. The proposed approach uses multiple inputs deep neural network architectures using a loss function, called mean-variance loss. The data set was collected from Chonnam National University Hwasun Hospital. The experiment results show the effectiveness of the proposed method with a mean absolute error of 3.40 years.
Keywords
Bone Age Estimation; Bone Scintigraphy; Mean-Variance Loss; Multiple Inputs Deep Neural Networks;
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