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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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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