Abstract
Cobb angle plays an important role in the diagnosis of scoliosis, which can effectively quantify the degree of scoliosis. Manual measurement of Cobb angles is time-consuming, and the results are also heavily affected by the expert’s choice. In this paper, we propose a spine curve guide framework to directly regress the cobb angle from single AP view X-rays images. We firstly design a segmentation network to accurately segment two spine boundary, and then aggregate the obtained boundary scoremap with the original spinal X-rays images to input another angle estimation network to make high-precision regression prediction for cobb angle. We evaluate our method in the AASCE19 challenge, and our result achieves 22.1775 SMAPE that shows strong competitiveness compared to other excellent methods.
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References
Chen, B., Xu, Q., Wang, L., Leung, S., Chung, J., Li, S.: An automated and accurate spine curve analysis system. IEEE Access 7, 124596–124605 (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
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
Vrtovec, T., Pernuš, F., Likar, B.: A review of methods for quantitative evaluation of spinal curvature. Eur. Spine J. 18(5), 593–607 (2009)
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, 101542 (2019)
Wu, Hongbo, Bailey, Chris, Rasoulinejad, Parham, Li, Shuo: Automatic Landmark Estimation for Adolescent Idiopathic Scoliosis Assessment Using BoostNet. In: Descoteaux, Maxime, Maier-Hein, Lena, Franz, Alfred, Jannin, Pierre, Collins, D.Louis, Duchesne, Simon (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)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Acknowledgement
This work was supported by National Natural Science Foundation of China (Grant No. 61671399) and by the Fundamental Research Funds for the Central Universities (Grant No. 20720190012).
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Wang, S., Huang, S., Wang, L. (2020). Spinal Curve Guide Network (SCG-Net) for Accurate Automated Spinal Curvature Estimation. In: Cai, Y., Wang, L., Audette, M., Zheng, G., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2019. Lecture Notes in Computer Science(), vol 11963. Springer, Cham. https://doi.org/10.1007/978-3-030-39752-4_13
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DOI: https://doi.org/10.1007/978-3-030-39752-4_13
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