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Region of Interest Localization for Bone Age Estimation Using Whole-Body Bone Scintigraphy
Thanh-Cong Do,양형정(Hyung Jeong Yang),김수형(Soo Hyung Kim),이귀상(Guee Sang Lee),강세령(Sae Ryung Kang),민정준(Jung Joon Min) 한국스마트미디어학회 2021 스마트미디어저널 Vol.10 No.2
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.