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An automated estimator for Cobb angle measurement using multi-task networks

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

Scoliosis is a medical condition where a person’s spine has a sideways curve. The Cobb angle quantifying the degree of spinal curvature is the gold standard for a scoliosis assessment. Recently, the deep learning methods based on segmentation and landmark estimation both achieve high performance for automated Cobb angle measurement on X-rays. However, we notice that these methods utilize segmentation and landmark information separately. In this light, we propose an automated architecture that uses combined segmentation with landmark information to estimate 68 landmarks of 17 vertebrae. In addition, we consider spinal curvature described by 68 landmarks as a constraint to estimate the Cobb angle. Extensive experiment results which test on 240 X-rays demonstrate that our method improves the landmark estimation performance effectively and reduces the Cobb angle error.

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References

  1. Clark EM, Taylor HJ, Harding I, Hutchinson J, Nelson I, Deanfield JE, Ness AR, Tobias JH (2014) Association between components of body composition and scoliosis: a prospective cohort study reporting differences identifiable before the onset of scoliosis. J Bone Miner Res 29(8):1729. https://doi.org/10.1002/jbmr.2207

    Article  Google Scholar 

  2. Konieczny MR, Senyurt H, Krauspe R (2013) Epidemiology of adolescent idiopathic scoliosis. J Child Orthop 7(1):3. https://doi.org/10.1007/s11832-012-0457-4

    Article  Google Scholar 

  3. Harrison Harrison DD, Cailliet R, Troyanovich SJ, Janik TJ, Holland B (2000) Cobb method or harrison posterior tangent method: which to choose for lateral cervical radiographic analysis. Spine (Phila. Pa. 1976). https://doi.org/10.1097/00007632-200008150-00011

    Article  Google Scholar 

  4. Cobb JR (1917) Outline for the study of bitumens. Sch Sci Math 17(1):31. https://doi.org/10.1111/j.1949-8594.1917.tb01839.x

    Article  Google Scholar 

  5. Sun H, Zhen X, Bailey C, Rasoulinejad P, Yin Y, Li S (2017) Direct estimation of spinal cobb angles by structured multi-output regression. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 10265:529–540. https://doi.org/10.1007/978-3-319-59050-9_42

    Article  Google Scholar 

  6. Wu H, Bailey C, Rasoulinejad P, Li S (2017) Automatic landmark estimation for adolescent idiopathic scoliosis assessment using BoostNet. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 10433:127–135. https://doi.org/10.1007/978-3-319-66182-7_15

    Article  Google Scholar 

  7. Wu H, Bailey C, Rasoulinejad P, Li S (2018) Automated comprehensive adolescent Idiopathic Scoliosis assessment using MVC-Net. Med Image Anal 48:1. https://doi.org/10.1016/j.media.2018.05.005

    Article  Google Scholar 

  8. Wang L, Xu Q, Leung S, Chung J, Chen B, Li S (2019) Accurate automated Cobb angles estimation using multi-view extrapolation net. Med Image Anal 58:101542. https://doi.org/10.1016/j.media.2019.101542

    Article  Google Scholar 

  9. Chen B, Xu Q, Wang L, Leung S, Chung J, Li S (2019) An automated and accurate spine curve analysis system. IEEE Access 7:124596. https://doi.org/10.1109/ACCESS.2019.2938402

    Article  Google Scholar 

  10. Okashi OA, Du H, Al-Assam H (2017) Automatic spine curvature estimation from X-ray images of a mouse model. Comput Methods Progr Biomed 140:175. https://doi.org/10.1016/j.cmpb.2016.12.010

    Article  Google Scholar 

  11. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 9351:234. https://doi.org/10.1007/978-3-319-24574-4_28

    Article  Google Scholar 

  12. Jegou S, Drozdzal M, Vazquez Romero A, Bengio Y (2017) the one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 2017:1175–1183. https://doi.org/10.1109/CVPRW.2017.156

    Article  Google Scholar 

  13. Zhang Z, Liu Q, Wang Y, Geosci IEEE (2018) Road extraction by deep residual U-Net. Remote Sens Lett 15(5):749. https://doi.org/10.1109/LGRS.2018.2802944

    Article  Google Scholar 

  14. Al Arif SMR, Knapp K, Slabaugh G (2018) Shape-aware deep convolutional neural network for vertebrae segmentation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 10734:12–24. https://doi.org/10.1007/978-3-319-74113-0_2

    Article  Google Scholar 

  15. Tu Y, Wang N, Tong F, Chen H (2019) Automatic measurement algorithm of scoliosis Cobb angle based on deep learning. J Phys Conf Ser 1187:42100. https://doi.org/10.1088/1742-6596/1187/4/042100

    Article  Google Scholar 

  16. Horng MH, Kuok CP, Fu MJ, Lin CJ, Sun YN (2019) Cobb angle measurement of spine from x-ray images using convolutional neural network. Comput Math Methods Med. https://doi.org/10.1155/2019/6357171

    Article  MATH  Google Scholar 

  17. He K, Gkioxari G, Dollar P, Girshick R (2017) Mask R-CNN. Proc IEEE Int Conf Comput Vis. https://doi.org/10.1109/ICCV.2017.322

    Article  Google Scholar 

  18. Pan Y, Chen Q, Chen T, Wang H, Zhu X, Fang Z, Lu Y (2019) Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. Eur Spine J 28(12):3035. https://doi.org/10.1007/s00586-019-06115-w

    Article  Google Scholar 

  19. Cho BH, Kaji D, Cheung ZB, Ye IB, Tang R, Ahn A, Carrillo O, Schwartz JT, Valliani AA, Oermann EK, Arvind V, Ranti D, Sun L, Kim JS, Cho SK (2019) Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision. Glob Spine J. https://doi.org/10.1177/2192568219868190

    Article  Google Scholar 

  20. Zhang K, Xu N, Yang G, Wu J, Fu X (2019) An automated Cobb angle estimation method using convolutional neural network with area limitation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 11769:775–783. https://doi.org/10.1007/978-3-030-32226-7_86

    Article  Google Scholar 

  21. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2015.7298965

    Article  Google Scholar 

  22. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on machine learning ICML, vol 1, p 448

  23. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929

    MathSciNet  MATH  Google Scholar 

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Acknowledgements

This study was supported by the National Key Research and Development Program of China (No. 2018Y-FC0116800), by Beijing Municipal Natural Science Foundation (No. L192026), by the Young Scientists Fund of the National Natural Science Foundation of China (No. 2019NSFC81901822) and by the Peking University Fund of Fostering Young Scholars’ Scientific & Technological Innovation (No. BMU2018PYB016).

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Authors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), BUPT, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiangling Fu & Guosheng Yang

  2. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Kailai Zhang & Ji Wu

  3. Peking University Third Hospital, Beijing, China

    Nanfang Xu

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  1. Xiangling Fu
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  2. Guosheng Yang
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  3. Kailai Zhang
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  4. Nanfang Xu
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  5. Ji Wu
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Correspondence to Ji Wu.

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Fu, X., Yang, G., Zhang, K. et al. An automated estimator for Cobb angle measurement using multi-task networks. Neural Comput & Applic 33, 4755–4761 (2021). https://doi.org/10.1007/s00521-020-05533-y

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  • DOI: https://doi.org/10.1007/s00521-020-05533-y

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