Abstract
Bone Age Assessment (BAA) is a pediatric examination performed to determine the difference between children’s skeletal bone age and chronological age, the inconsistency between the two will often indicate either hormonal problems or abnormalities in the skeletal system maturity. Previous works to upgrade the tedious traditional techniques had failed to address the human expert inter-observer variability in order to significantly refine BAA evaluations. This paper proposes a deep learning method that detects and segments carpal bones as the region of interests within the left hand and wrist radiographs, and then feed the image data into a deep convolutional neural network. Tests are then made to determine whether it is more efficient to use full hand radiographs or segmented regions of interest, and also made comparisons with some CNN models. Evaluations show that the proposed method can dramatically increase the accuracy.
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References
Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)
Gaskin, C., Kahn, S., Bertozzi, J., Bunch, P.: Skeletal Development of the Hand and Wrist: A Radiographic Atlas and Digital Bone Age Companion (2011)
Greenspan, H., van Ginneken, B., Summers, R.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)
Greulich, W., Pyle, S.: Radiographic Atlas of Skeletal Development of the Hand and Wrist. Stanford University Press, Stanford (1959)
Khan, K., Elayappen, A.: Bone Growth Estimation Using Radiology (Greulich-Pyle and Tanner-Whitehouse Methods). In: Preedy, V. (ed.) Handbook of Growth and Growth Monitoring in Health and Disease. Springer, New York (2012). https://doi.org/10.1007/978-1-4419-1795-9_176
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Lee, H., et al.: Fully automated deep learning system for bone age assessment. J. Digit. Imaging 30(4), 427–441 (2017)
Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)
Bonawitz, C.A.: Practical pediatric imaging: diagnostic radiology of infants and children. Acad. Radiol. 6(10), 607 (1999)
Poznanski, A.: Assessment of skeletal maturity and prediction of adult height (TW2 method). Am. J. Dis. Child. 131(9), 1041–1042 (1977)
Seok, J., Hyun, B., Kasa-Vubu, J., Girard, A.: Automated classification system for bone age x-ray images. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 208–213. IEEE (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICML (2015)
Somkantha, K., Theera-Umpon, N., Auephanwiriyakul, S.: Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression. J. Digit. Imaging 24(6), 1044–1058 (2011)
Spampinato, C., Palazzo, S., Giordano, D.: Deep learning for automated skeletal bone age assessment in x-ray images. Med. Image Anal. 36, 41–51 (2016)
Thodberg, H.H., Kreiborg, S., Juul, A., Pedersen, K.: The bonexpert method for automated determination of skeletal maturity. IEEE Trans. Med. Imaging 28(1), 52–66 (2009)
Zhang, A., Gertych, A., Liu, B.J.: Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones. Comput. Med. Imaging Graph. 31(4–5), 299–310 (2007)
Acknowledgments
The work is supported by Zhejiang Provincial Natural Science Foundation of China under grants No. LY18F020034, LY18F020032, and National Natural Science Foundation of China under grants No. 61502424 and No. 61801428 and partially supported by the Ministry of Education of China under grant of No. 2017PT18 and the Zhejiang University Education Foundation under grant of No. K18-511120-004 and No. K17-511120-017. This work is also supported by The Research of Real Doctor AI Research Center.
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Hao, P., Chen, Y., Chokuwa, S., Wu, F., Bai, C. (2018). Skeletal Bone Age Assessment Based on Deep Convolutional Neural Networks. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_38
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DOI: https://doi.org/10.1007/978-3-030-00767-6_38
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