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THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy

  • Do, Thanh-Cong (Dept. of AI Convergence, Chonnam National University) ;
  • Yang, Hyung Jeong (Dept. of AI Convergence, Chonnam National University) ;
  • Kim, Soo Hyung (Dept. of AI Convergence, Chonnam National University) ;
  • Lee, Guee Sang (Dept. of AI Convergence, Chonnam National University) ;
  • Kang, Sae Ryung (Dept. of Nuclear Medicine, Chonnam National University Hwasun Hospital) ;
  • Min, Jung Joon (Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School)
  • Received : 2021.04.02
  • Accepted : 2021.04.22
  • Published : 2021.06.30
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Abstract

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (NRF-2020R1A2B5B01002085) and This study was financially supported by Chonnam National University (Grant number: 2018-3359)

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