Automated Segmentation of Intervertebral Disc Using Fully Dilated Separable Deep Neural Networks

  • Huan Wang
  • Ran Gu
  • Zhongyu LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11397)


Accurate segmentation of intervertebral discs is a critical task in clinical diagnosis and treatment. Despite recent progress in applying deep learning to the segmentation of multiple natural image scenarios, addressing of the intervertebral disc segmentation with a small-sized training set are still challenging problems. In this paper, a new framework with fully dilated separable convolution (FDS-CNN) is proposed for the automated segmentation of the intervertebral disc using a small-sized training set. Firstly, a fully dilated separable convolutional network is designed to effectively prevent the loss of context information by reducing the number of down-sampling. Secondly, a multi-modality data fusion and augmentation strategy are proposed, which can increase the number of samples, as well as make full use of multi-modality image data. Experimental results validate the proposed framework in the MICCAI 2018 Challenge on Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR Images, demonstrating excellent performance in comparison with other related segmentation methods.


Intervertebral disc Dilated separable convolution Semantic segmentation Multi-modality data fusion 


  1. 1.
    Luoma, K., Riihimäki, H., Luukkonen, R., Raininko, R., Viikarijuntura, E., Lamminen, A.: Low back pain in relation to lumbar disc degeneration. Spine 25(4), 487–492 (2000)CrossRefGoogle Scholar
  2. 2.
    Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011). Scholar
  3. 3.
    Law, M.W., Tay, K., Leung, A., Garvin, G.J., Li, S.: Intervertebral disc segmentation in MR images using anisotropic oriented flux. Med. Image Anal. 17(1), 43–61 (2013)CrossRefGoogle Scholar
  4. 4.
    Chevrefils, C., Chériet, F., Grimard, G., Aubin, C.-E.: Watershed segmentation of intervertebral disk and spinal canal from MRI images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1017–1027. Springer, Heidelberg (2007). Scholar
  5. 5.
    Lin, G., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5168–5177 (2017)Google Scholar
  6. 6.
    Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 2881–2890 (2017)Google Scholar
  7. 7.
    Wang, P., et al.: Understanding convolution for semantic segmentation. arXiv preprint, arXiv: 1702.08502 (2017)
  8. 8.
    Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous convolution for semantic image segmentation. arXiv preprint, arXiv: 1706.05587 (2017)
  9. 9.
    Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with Atrous separable convolution for semantic image segmentation. arXiv preprint, arXiv: 1802.02611. (2018)
  10. 10.
    Chen, H., Dou, Q., Wang, X., Qin, J., Cheng, J.C.Y., Heng, P.A.: 3D fully convolutional networks for intervertebral disc localization and segmentation. In: MICCAI Workshop MIAR, pp. 375–382 (2016)Google Scholar
  11. 11.
    Li, X., Dou, Q., Chen, H., Fu, C.W., Heng, P.A.: Multi-scale and modality dropout learning for intervertebral disc localization and segmentation. In: MICCAI Workshop CSI, pp. 85–91 (2016)Google Scholar
  12. 12.
    Zeng, G., Zheng, G.: DSMS-FCN: a deeply supervised multi-scale fully convolutional network for automatic segmentation of intervertebral disc in 3D MR images. In: Glocker, B., Yao, J., Vrtovec, T., Frangi, A., Zheng, G. (eds.) MSKI 2017. LNCS, vol. 10734, pp. 148–159. Springer, Cham (2018). Scholar
  13. 13.
    Liao, H., Mesfin, A., Luo, J.: Joint vertebrae identification and localization in spinal CT images by combining short-and long-range contextual Information. IEEE Trans. Med. Imaging 37(5), 1266–1275 (2018)CrossRefGoogle Scholar
  14. 14.
    Zeng, G., Yang, X., Li, J., Yu, L., Heng, P.-A., Zheng, G.: 3D U-net with multi-level deep supervision: fully automatic segmentation of proximal femur in 3D MR images. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 274–282. Springer, Cham (2017). Scholar
  15. 15.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  16. 16.
    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
  17. 17.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448–456 (2015)Google Scholar
  18. 18.
    Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint, arXiv: 1610.02357 (2017)
  19. 19.
    Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, arXiv: 1704.04861 (2017)
  20. 20.
    Li, Z., Zhang, X., Müller, H., Zhang, S.: Large-scale retrieval for medical image analytics: a comprehensive review. Med. Image Anal. 43, 66–84 (2018)CrossRefGoogle Scholar
  21. 21.
    IVDM3Seg Homepage.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mechanical and Electrical EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations