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International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging

CSI 2019: Computational Methods and Clinical Applications for Spine Imaging pp 101–106Cite as

A Coarse-to-Fine Deep Heatmap Regression Method for Adolescent Idiopathic Scoliosis Assessment

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11963))

Abstract

Spinal anterior-posterior x-ray CT imaging is an appealing tool to aid diagnosis and elucidate Adolescent Idiopathic Scoliosis (AIS) Assessment. In this paper, we propose an automatic detection method for AIS assessment from X-ray CT images. Our deep learning based coarse-to-fine heatmaps regression method achieves symmetric mean absolute percentage error (SMAPE) of 24.7987 in the grand challenge AASCE 2019.

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References

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Zhusi Zhong, Jie Li, Zhenxi Zhang & Xinbo Gao

  2. Perelman School of Medicine, University of Pennsylvania, Hamilton, PA, 19104, USA

    Zhicheng Jiao

Authors
  1. Zhusi Zhong
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  2. Jie Li
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  3. Zhenxi Zhang
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  4. Zhicheng Jiao
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  5. Xinbo Gao
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Corresponding author

Correspondence to Xinbo Gao .

Editor information

Editors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, MA, USA

    Yunliang Cai

  2. Xiamen University, Xiamen, China

    Liansheng Wang

  3. Old Dominion University, Norfolk, VA, USA

    Michel Audette

  4. Shanghai Jiao Tong University, Shanghai, China

    Guoyan Zheng

  5. Western University, London, ON, Canada

    Shuo Li

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Zhong, Z., Li, J., Zhang, Z., Jiao, Z., Gao, X. (2020). A Coarse-to-Fine Deep Heatmap Regression Method for Adolescent Idiopathic Scoliosis Assessment. 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_12

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  • DOI: https://doi.org/10.1007/978-3-030-39752-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39751-7

  • Online ISBN: 978-3-030-39752-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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