Abstract
Recently, deep learning for spinal measurement in scoliosis achieved huge success. However, we notice that existing methods suffer low performance on lateral X-rays because of severe occlusion. In this paper, we propose the automated Landmark Estimation and Correction Network (LEC-Net) based on a convolutional neural network (CNN) to estimate landmarks on lateral X-rays. The framework consists of two parts (1) a landmark estimation network (LEN) and (2) a landmark correction network (LCN). The LEN first estimates 68 landmarks of 17 vertebrae (12 thoracic vertebrae and 5 lumbar vertebrae) per image. These landmarks may include some failed points on the area with occlusion. Then the LCN estimates the clinical parameters by considering the spinal curvature described by 68 landmarks as a constraint. Extensive experiment results which test on 240 lateral X-rays demonstrate that our method improves the landmark estimation accuracy and achieves high performance of clinical parameters on X-rays with severe occlusion. Implementation code is available at https://github.com/xiaoyanermiemie/LEN-LCN.
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
Similar content being viewed by others
Notes
- 1.
http://spineweb.digitalimaginggroup.ca/, Dataset 16.
References
Al Okashi, O., Du, H., Al-Assam, H.: Automatic spine curvature estimation from X-ray images of a mouse model. Comput. Methods Programs Biomed. 140, 175–184 (2017)
Cho, B.H., et al.: Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision. Glob. Spine J. 10, 611–618 (2019)
Cobb, J.: Outline for the study of scoliosis. Instr. Course Lect. AAOS 5, 261–275 (1948)
Galbusera, F., et al.: Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur. Spine J. 28(5), 951–960 (2019). https://doi.org/10.1007/s00586-019-05944-z
Harrison, D.E., Harrison, D.D., Cailliet, R., Troyanovich, S.J., Janik, T.J., Holland, B.: Cobb method or Harrison posterior tangent method: which to choose for lateral cervical radiographic analysis. Spine 25(16), 2072–2078 (2000)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Jian, S.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision & Pattern Recognition (2016)
Horng, M.H., Kuok, C.P., Fu, M.J., Lin, C.J., Sun, Y.N.: Cobb angle measurement of spine from X-ray images using convolutional neural network. Comput. Math. Methods Med. (2019)
Konieczny, M.R., Senyurt, H., Krauspe, R.: Epidemiology of adolescent idiopathic scoliosis. J. Child. Orthop. 7(1), 3–9 (2012). https://doi.org/10.1007/s11832-012-0457-4
Liu, R., et al.: An intriguing failing of convolutional neural networks and the coordconv solution. In: Advances in Neural Information Processing Systems, pp. 9605–9616 (2018)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Pan, Y., et al.: Evaluation of a computer-aided method for measuring the cobb angle on chest X-rays. Eur. Spine J. 28(12), 3035–3043 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sun, H., Zhen, X., Bailey, C., Rasoulinejad, P., Yin, Y., Li, S.: Direct estimation of spinal cobb angles by structured multi-output regression. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 529–540. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_42
Tu, Y., Wang, N., Tong, F., Chen, H.: Automatic measurement algorithm of scoliosis cobb angle based on deep learning. In: Journal of Physics: Conference Series, vol. 1187. IOP Publishing (2019)
Wang, L., Xu, Q., Leung, S., Chung, J., Chen, B., Li, S.: Accurate automated cobb angles estimation using multi-view extrapolation net. Med. Image Anal. 58 (2019)
Wu, H., Bailey, C., Rasoulinejad, P., Li, S.: Automatic landmark estimation for adolescent idiopathic scoliosis assessment using BoostNet. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 127–135. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_15
Wu, H., Bailey, C., Rasoulinejad, P., Li, S.: Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-Net. Med. Image Anal. 48, 1–11 (2018)
Zhang, K., Xu, N., Yang, G., Wu, J., Fu, X.: An automated cobb angle estimation method using convolutional neural network with area limitation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 775–783. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_86
Acknowledgments
This study was supported by the National Key Research and Development Program of China (No.2018YFC0116800), by Beijing Municipal Natural Science Foundation (No. L192026), by the Young Scientists Fund of the National Natural Science Foundation of China (No.2019NSFC81901822) and by the Peking University Fund of Fostering Young Scholars’ Scientific & Technological Innovation (No. BMU2018PYB016).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, G., Fu, X., Xu, N., Zhang, K., Wu, J. (2020). A Landmark Estimation and Correction Network for Automated Measurement of Sagittal Spinal Parameters. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-63820-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63819-1
Online ISBN: 978-3-030-63820-7
eBook Packages: Computer ScienceComputer Science (R0)