TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

Deep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they hav...

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Main Authors:	Sung Joon Son, Youngmin Song, Namgi Kim, Younghae Do, Nojun Kwak, Mu Sook Lee, Byoung-Dai Lee
Format:	Article
Language:	English
Published:	IEEE 2019-01-01
Series:	IEEE Access
Subjects:	Bone age assessment deep learning GP TW3
Online Access:	https://ieeexplore.ieee.org/document/8660640/

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spelling	doaj-4d111a6d64544b398518587c143331c52021-03-29T22:58:24ZengIEEEIEEE Access2169-35362019-01-017333463335810.1109/ACCESS.2019.29031318660640TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural NetworksSung Joon Son0Youngmin Song1Namgi Kim2Younghae Do3Nojun Kwak4Mu Sook Lee5Byoung-Dai Lee6https://orcid.org/0000-0002-4028-6168Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaDepartment of Computer Science, Kyonngi University, Suwon, South KoreaDepartment of Computer Science, Kyonngi University, Suwon, South KoreaDepartment of Mathematics, Kyungpook National University, Daegu, South KoreaGraduate School of Convergence Science and Technology, Seoul National University, Seoul, South KoreaHuman Medical Imaging Center, Seoul, South KoreaDepartment of Computer Science, Kyonngi University, Suwon, South KoreaDeep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they have shown promising results. In this paper, we propose a complete end-to-end BAA system to automate the entire process of the Tanner-Whitehouse 3 method, starting from localization of the epiphysis-metaphysis growth regions within 13 different bones and ending with estimation of the corresponding BA. Specific modifications to the CNNs and other stages are proposed to improve results. In addition, an annotated database of 3300 X-ray images is built to train and evaluate the system. The experimental results show that the average top-1 and top-2 prediction accuracies for skeletal bone maturity levels for 13 regions of interest are 79.6% and 97.2%, respectively. The mean absolute error and root mean squared error in age prediction are 0.46 years and 0.62 years, respectively, and accuracy within one year of the ground truth of 97.6% is achieved. The proposed system is shown to outperform a commercially available Greulich-Pyle-based system, demonstrating the potential for practical clinical use.https://ieeexplore.ieee.org/document/8660640/Bone age assessmentdeep learningGPTW3
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Sung Joon Son Youngmin Song Namgi Kim Younghae Do Nojun Kwak Mu Sook Lee Byoung-Dai Lee
spellingShingle	Sung Joon Son Youngmin Song Namgi Kim Younghae Do Nojun Kwak Mu Sook Lee Byoung-Dai Lee TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks IEEE Access Bone age assessment deep learning GP TW3
author_facet	Sung Joon Son Youngmin Song Namgi Kim Younghae Do Nojun Kwak Mu Sook Lee Byoung-Dai Lee
author_sort	Sung Joon Son
title	TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks
title_short	TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks
title_full	TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks
title_fullStr	TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks
title_full_unstemmed	TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks
title_sort	tw3-based fully automated bone age assessment system using deep neural networks
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2019-01-01
description	Deep learning technology has rapidly evolved in recent years. Bone age assessment (BAA) is a typical object detection and classification problem that would benefit from deep learning. Convolutional neural networks (CNNs) and their variants are hence increasingly used for automating BAA, and they have shown promising results. In this paper, we propose a complete end-to-end BAA system to automate the entire process of the Tanner-Whitehouse 3 method, starting from localization of the epiphysis-metaphysis growth regions within 13 different bones and ending with estimation of the corresponding BA. Specific modifications to the CNNs and other stages are proposed to improve results. In addition, an annotated database of 3300 X-ray images is built to train and evaluate the system. The experimental results show that the average top-1 and top-2 prediction accuracies for skeletal bone maturity levels for 13 regions of interest are 79.6% and 97.2%, respectively. The mean absolute error and root mean squared error in age prediction are 0.46 years and 0.62 years, respectively, and accuracy within one year of the ground truth of 97.6% is achieved. The proposed system is shown to outperform a commercially available Greulich-Pyle-based system, demonstrating the potential for practical clinical use.
topic	Bone age assessment deep learning GP TW3
url	https://ieeexplore.ieee.org/document/8660640/
work_keys_str_mv	AT sungjoonson tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT youngminsong tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT namgikim tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT younghaedo tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT nojunkwak tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT musooklee tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks AT byoungdailee tw3basedfullyautomatedboneageassessmentsystemusingdeepneuralnetworks
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TW3-Based Fully Automated Bone Age Assessment System Using Deep Neural Networks

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