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Automatic Cobb Angle Detection Using Vertebra Detector and Vertebra Corners Regression

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


Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature. Current gold standard is to manually estimate Cobb Angles in spinal X-ray images which is time consuming and has high inter-rater variability. We propose an automatic method with a novel framework that first detects vertebrae as objects followed by a landmark detector that estimates the 4 landmark corners of each vertebra separately. Cobb Angles are calculated using the slope of each vertebra obtained from the predicted landmarks. For inference on test data, we perform pre and post processings that include cropping, outlier rejection and smoothing of the predicted landmarks. The results were assessed in AASCE MICCAI challenge 2019 which showed a promise with a SMAPE score of 25.69 on the challenge test set.


  • Scoliosis
  • Landmark
  • Object detection
  • Cobb angle

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  • DOI: 10.1007/978-3-030-39752-4_9
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This work is supported by NVIDIA GPU donation. We also thank Pro-Mech Minds & Engineering Services for agreeing to partially fund conference visit expenses for presenting this work.

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Correspondence to Bidur Khanal .

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Khanal, B., Dahal, L., Adhikari, P., Khanal, B. (2020). Automatic Cobb Angle Detection Using Vertebra Detector and Vertebra Corners Regression. 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.

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