An Automated Cobb Angle Estimation Method Using Convolutional Neural Network with Area Limitation

  • Kailai Zhang
  • Nanfang Xu
  • Guosheng Yang
  • Ji WuEmail author
  • Xiangling Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Cobb angle measurement is the gold standard for the idiopathic scoliosis assessment, and the measurement result is very important for the surgical planning and medical curing. Currently, the Cobb angle is measured manually by physicians. They find the four landmarks on each vertebra and calculate the Cobb angle by rules, which is time-consuming and unreliable. In this paper, we apply the convolutional neural network (CNN) to find the landmarks automatically based on anterior-posterior view X-rays, then output the Cobb angle results. The X-rays always have too much noise, which has a strong influence on the landmark estimation. Addressing this problem, we first detect each vertebra bounding box to provide an area limitation. Then we use the CNN with an enhancement module to find the landmarks on detected vertebra bounding boxes, which can remove the noise in the background. Our experiment results show that our two-stage framework achieves precise landmark location and small error on Cobb angle estimation. Therefore our method can provide reliable assistance for the physicians.


Cobb angle Convolutional neural network Area limitation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kailai Zhang
    • 1
  • Nanfang Xu
    • 3
  • Guosheng Yang
    • 4
  • Ji Wu
    • 1
    • 2
    Email author
  • Xiangling Fu
    • 4
  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Institute for Precision MedicineTsinghua UniversityBeijingChina
  3. 3.Peking University Third HospitalBeijingChina
  4. 4.School of Software EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

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