Abstract
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. Traditional clinical testing methods are time-consuming and labor-intensive, and there will be operator errors because of the subjective factors of the physicians. The existing automatic bone age detection methods based on automatic extraction of clinical features also has the problems of low accuracy and difficult generalization due to inaccurate feature extraction. In this paper, we propose an end-to-end automatic bone age detection method based on deep learning to process hand bone X-ray images. A Convolutional Block Attention Module (CBAM) is added to the basic model of Inception Resnet v2, the Softmax network layer is changed to the Mean Absolute Error (MAE) index output, and the mean square error loss function is used to evaluate the performance of the bone age detection regression problem. A new stratified k-fold cross validation method is proposed to cover training models on the public dataset of bone age for all races, genders and age ranges. It shows that the MAE between the detected bone age and the labeled bone age is 0.34 years in the results, which is better than the current bone age evaluation method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Martin, D.D., Deusch, D., Schweizer, R., et al.: Clinical application of automated Greulich-Pyle bone age determination in children with short stature[J]. Pediatr. Radiol. 39(6), 598–607 (2009)
Liu, J., Liu, J.: A review of the development of bone age assessment system[J]. Life Science Instruments 15(2), 9–13 (2017)
Zhang, A., Gertych, A., Liu, B.J.: Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones[J]. Comput. Med. Imaging Graph. 31(4–5), 299–310 (2007)
Garn, S.M.: Radiographic atlas of skeletal development of the hand and wrist[J]. Am. J. Hum. Genet. 11(3), 282 (1959)
Kim, S.Y., Oh, Y.J., Shin, J.Y., et al.: Comparison of the Greulich-Pyle and Tanner Whitehouse (TW3) Methods in Bone Age Assessment[J]. Journal of Korean Society of Pediatric Endocrinology 13(1), 50–55 (2008)
Christoforidis, A., Badouraki, M., Katzos, G., et al.: Bone age estimation and prediction of final height in patients with β-thalassaemia major: a comparison between the two most common methods[J]. Pediatric Radiology. 37(12) (2007)
King, D. G., Steventon, D. M., O'sullivan, M.P. et al.: Reproducibility of bone ages when performed by radiology registrars: an audit of Tanner and Whitehouse II versus Greulich and Pyle methods[J]. British J. Radiol. 67(801), 848–851.
Pietka, E., Gertych, A., Pospiech, S., et al.: Computer-assisted bone age assessment: Image preprocessing and epiphyseal/metaphyseal ROI extraction[J]. IEEE Trans. Med. Imaging 20(8), 715–729 (2001)
Pietka, E., Pospiech-Kurkowska, S., Gertych, A., et al.: Integration of computer assisted bone age assessment with clinical PACS[J]. Comput. Med. Imaging Graph. 27(2–3), 217–228 (2003)
Gertych, A., Zhang, A., Sayre, J., et al.: Bone age assessment of children using a digital hand atlas[J]. Comput. Med. Imaging Graph. 31(4–5), 322–331 (2007)
Seok, J., Kasa-Vubu, J., DiPietro, M., et al.: Expert system for automated bone age determination[J]. Expert Syst. Appl. 50, 75–88 (2016)
Thodberg, H.H., Kreiborg, S., Juul, A., et al.: The BoneXpert method for automated determination of skeletal maturity[J]. IEEE Trans. Med. Imaging 28(1), 52–66 (2008)
Spampinato, C., Palazzo, S., Giordano, D., et al.: Deep learning for automated skeletal bone age assessment in X-ray images[J]. Med. Image Anal. 36, 41–51 (2017)
Szegedy, C., Ioffe, S., Vanhoucke, V., et al.: Inception-ResNet and the impact of residual connections on learning[J]. arXiv preprint arXiv:1602.07261.
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826 (2016)
Woo, S, Park, J., Lee, J.Y., et al.: Cbam: Convolutional block attention module[C]. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, J., Mei, L., Zhang, J. (2021). Skeletal Bone Age Assessment in Radiographs Based on Convolutional Neural Networks. In: Lim, C.T., Leo, H.L., Yeow, R. (eds) 17th International Conference on Biomedical Engineering. ICBME 2019. IFMBE Proceedings, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-030-62045-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-62045-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62044-8
Online ISBN: 978-3-030-62045-5
eBook Packages: EngineeringEngineering (R0)