Abstract
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.
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References
Greiner, K.A.: Adolescent idiopathic scoliosis: radiologic decision-making. Am. Fam. Physician 65, 1817–22 (2002)
Loder, R.T., et al.: The assessment of intraobserver and interobserver error in the measurement of noncongenital scoliosis in children \(\le \) 10 years of age. Spine 29(22), 2548–2553 (2004)
Sardjono, T.A., et al.: Automatic cobb angle determination from radiographic images. Spine (Phila Pa 1976) 38, E1256–E1262 (2013)
Allen, S., et al.: Validity and reliability of active shape models for the estimation of cobb angle in patients with adolescent idiopathic scoliosis. J. Digit. Imaging 21, 208–18 (2008)
Zhang, J., et al.: Automatic cobb measurement of scoliosis based on fuzzy hough transform with vertebral shape prior. J. Digit. Imaging 22, 463–72 (2009)
Wu, H., Bailey, C., Rasoulinejad, P., Li, S.: Automatic landmark estimation for adolescent idiopathic scoliosis assessment using boostnet. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 127–135. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_15
Sun, H., Zhen, X., Bailey, C., Rasoulinejad, P., Yin, Y., Li, S.: Direct estimation of spinal cobb angles by structured multi-output regression. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 529–540. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_42
Alansary, A., et al.: Evaluating reinforcement learning agents for anatomical landmark detection. MedIA 53, 156–164 (2019)
Li, Y., et al.: Fast multiple landmark localisation using a patch-based iterative network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 563–571. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_64
Payer, C., et al.: Integrating spatial configuration into heatmap regression based CNNS for landmark localization. MedIA 54, 207–219 (2019)
Sa, R., et al.: Intervertebral disc detection in X-ray images using faster R-CNN. In: Conference Proceedings of IEEE Engineering in Medicine and Biology Society, pp. 564–567 (2017)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE TPAMI 39(6), 1137–1149 (2017)
Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017)
Acknowledgements
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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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. https://doi.org/10.1007/978-3-030-39752-4_9
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DOI: https://doi.org/10.1007/978-3-030-39752-4_9
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