Shape-Aware Deep Convolutional Neural Network for Vertebrae Segmentation

  • S. M. Masudur Rahman Al ArifEmail author
  • Karen Knapp
  • Greg Slabaugh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10734)


Shape is an important characteristic of an object, and a fundamental topic in computer vision. In image segmentation, shape has been widely used in segmentation methods, like the active shape model, to constrain a segmentation result to a class of learned shapes. However, to date, shape has been underutilized in deep segmentation networks. This paper addresses this gap by introducing a shape-aware term in the segmentation loss function. A deep convolutional network has been adapted in a novel cervical vertebrae segmentation framework and compared with traditional active shape model-based methods. The proposed framework has been trained on an augmented dataset of 26370 vertebrae and tested on 792 vertebrae collected from a total of 296 real-life emergency room lateral cervical X-ray images. The proposed framework achieved an average error of 1.11 pixels, signifying a 36% improvement over the traditional methods. The introduction of the novel shape-aware term in the loss function significantly improved the performance by further 12%, achieving an average error of only 0.99 pixel.


Convolutional neural networks Vertebrae Segmentation Shape-aware X-rays 



We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.


  1. 1.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., et al. (eds.) Proceedings of the Neural Information Processing Systems - NIPS 2012, vol. 25, pp. 1097–1105 (2012)Google Scholar
  2. 2.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556
  3. 3.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2015, pp. 1–9. IEEE (2015)Google Scholar
  4. 4.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2016, pp. 770–778. IEEE (2016)Google Scholar
  5. 5.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)CrossRefGoogle Scholar
  6. 6.
    Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision - ICCV 2015, pp. 1529–1537. IEEE (2015)Google Scholar
  7. 7.
    Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision - ICCV 2015, pp. 1520–1528. IEEE (2015)Google Scholar
  8. 8.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  9. 9.
    BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 460–468. Springer, Cham (2016). Scholar
  10. 10.
    Yushkevich, P., Piven, J., Hazlett, H., Smith, R., Ho, S., Gee, J., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar
  11. 11.
    Pluempitiwiriyawej, C., Moura, J., Wu, Y.J., Ho, C.: STACS: new active contour scheme for cardiac MR image segmentation. IEEE Trans. Med. Imaging 24(5), 593–603 (2005)CrossRefGoogle Scholar
  12. 12.
    Weese, J., Wächter-Stehle, I., Zagorchev, L., Peters, J.: Shape-Constrained deformable models and applications in medical imaging. In: Li, S., Tavares, J.M.R.S. (eds.) Shape Analysis in Medical Image Analysis. LNCVB, vol. 14, pp. 151–184. Springer, Cham (2014). Scholar
  13. 13.
    Farag, A.A., Shalaby, A., El Munim, H.A., Farag, A.: Variational shape representation for modeling, elastic registration and segmentation. In: Li, S., Tavares, J.M.R.S. (eds.) Shape Analysis in Medical Image Analysis. LNCVB, vol. 14, pp. 95–121. Springer, Cham (2014). Scholar
  14. 14.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Understand. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  15. 15.
    Benjelloun, M., Mahmoudi, S., Lecron, F.: A framework of vertebra segmentation using the active shape model-based approach. Int. J. Biomed. Imaging 2011, 621905 (2011)CrossRefGoogle Scholar
  16. 16.
    Larhmam, M.A., Mahmoudi, S., Benjelloun, M.: Semi-automatic detection of cervical vertebrae in X-ray images using generalized hough transform. In: Proceedings of the 3rd IEEE International Conference on Image Processing Theory, Tools and Applications - IPTA 2012, pp. 396–401. IEEE (2012)Google Scholar
  17. 17.
    Roberts, M., Cootes, T., Adams, J.: Vertebral morphometry: semiautomatic determination of detailed shape from dual-energy X-ray absorptiometry images using active appearance models. Invest. Radiol. 41(12), 849–859 (2006)CrossRefGoogle Scholar
  18. 18.
    Roberts, M., Pacheco, E., Mohankumar, R., Cootes, T., Adams, J.: Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation. Osteoporos. Int. 21(12), 2037–2046 (2010)CrossRefGoogle Scholar
  19. 19.
    Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic location of vertebrae on DXA images using random forest regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 361–368. Springer, Heidelberg (2012). Scholar
  20. 20.
    Bromiley, P., Adams, J., Cootes, T.: Localisation of vertebrae on DXA images using constrained local models with random forest regression voting. In: Yao, J., et al. (eds.) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 20, pp. 159–171. Springer, Cham (2015). Scholar
  21. 21.
    Al Arif, S.M.M.R., Gundry, M., Knapp, K., Slabaugh, G.: Improving an active shape model with random classification forest for segmentation of cervical vertebrae. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds.) CSI 2016. LNCS, vol. 10182, pp. 3–15. Springer, Cham (2016). Scholar
  22. 22.
    Bromiley, P.A., Kariki, E.P., Adams, J.E., Cootes, T.F.: Fully automatic localisation of vertebrae in CT images using random forest regression voting. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds.) CSI 2016. LNCS, vol. 10182, pp. 51–63. Springer, Cham (2016). Scholar
  23. 23.
    Mahmoudi, S., Lecron, F., Manneback, P., Benjelloun, M., Mahmoudi, S.: GPU-based segmentation of cervical vertebra in X-ray images. In: Proceedings of the IEEE International Conference on Cluster Computing Workshops and Posters - CLUSTER WORKSHOPS, pp. 1–8. IEEE (2010)Google Scholar
  24. 24.
    Ruder, S.: An overview of gradient descent optimization algorithms (2016). arXiv:1609.04747

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • S. M. Masudur Rahman Al Arif
    • 1
    Email author
  • Karen Knapp
    • 2
  • Greg Slabaugh
    • 1
  1. 1.City, University of LondonLondonUK
  2. 2.University of ExeterExeterUK

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