Evaluation and Comparison of Automatic Intervertebral Disc Localization and Segmentation methods with 3D Multi-modality MR Images: A Grand Challenge

  • Guodong ZengEmail author
  • Daniel Belavy
  • Shuo Li
  • Guoyan Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11397)


The localization and segmentation of Intervertebral Discs (IVDs) with 3D Multi-modality MR Images are critically important for spine disease diagnosis and measurements. Manual annotation is a tedious and laborious procedure. There exist automatic IVD localization and segmentation methods on multi-modality IVD MR images, but an objective comparison of such methods is lacking. Thus we organized the following challenge: Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR Images, held at the 2018 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018). Our challenge ensures an objective comparison by running 8 submitted methods with docker container. Experimental results show that overall the best localization method achieves a mean localization distance of 0.77 mm and the best segmentation method achieves a mean Dice of 90.64% and a mean average absolute distance of 0.60 mm, respectively. This challenge still keeps open for future submission and provides an online platform for methods comparison.


Intervertebral disc MRI Localization Segmentation Multi-modality Challenge 


  1. 1.
    An, H.S., et al.: Introduction: disc degeneration: summary. Spine 29(23), 2677–2678 (2004)CrossRefGoogle Scholar
  2. 2.
    Emch, T.M., Modic, M.T.: Imaging of lumbar degenerative disk disease: history and current state. Skelet. Radiol. 40(9), 1175 (2011)CrossRefGoogle Scholar
  3. 3.
    Zheng, G., et al.: Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: a grand challenge. Med. Image Anal. 35, 327–344 (2017)CrossRefGoogle Scholar
  4. 4.
    Belavỳ, D.L., Armbrecht, G., Felsenberg, D.: Incomplete recovery of lumbar intervertebral discs 2 years after 60-day bed rest. Spine 37(14), 1245–1251 (2012)CrossRefGoogle Scholar
  5. 5.
    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: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds.) CSI 2016. LNCS, vol. 10182, pp. 85–91. Springer, Cham (2016). CrossRefGoogle Scholar
  6. 6.
    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). CrossRefGoogle Scholar
  7. 7.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
  8. 8.
    Liu, C.: IVDM3Seg Challenge MICCAI 2018: Method Description of Team Changliu (2018).
  9. 9.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, vol. 1, p. 3 (2017)Google Scholar
  10. 10.
    Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: DenseASPP for semantic segmentation in street scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3684–3692 (2018)Google Scholar
  11. 11.
    Gao, Y.: IVDM3Seg Challenge MICCAI 2018: Method Description of Team gaoyunhe\(\_\)cuhk (2018).
  12. 12.
    Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., Ayed, I.B.: HyperDense-net: a hyper-densely connected CNN for multi-modal image segmentation. arXiv preprint arXiv:1804.02967 (2018)
  13. 13.
    Dolz, J., Desrosiers, C., Ayed, I.B.: HD-UNet: hyper-dense UNet with asymmetric convolutions for multi-modal intervertebral disc segmentation (2018).
  14. 14.
    Carlinet, E., Géraud, T.: Intervertebral Disc Segmentation Using Mathematical Morphology (2018).
  15. 15.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  16. 16.
    Mader, A.O., Lorenz, C., Meyer, C.: Segmenting Labeled Intervertebral Discs in Multi Modality MR Images (2018).
  17. 17.
    Georgiev, N., Asenov, A.: Automatic Segmentation of Lumbar Spine 3D MRI Using Ensemble of 2D Algorithms (2018).
  18. 18.
    Iriondo, C., Girard, M.: Vesalius: VNet-based fully automatic segmentation of intervertebral discs in multimodality MR images (2018).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guodong Zeng
    • 1
    Email author
  • Daniel Belavy
    • 2
  • Shuo Li
    • 3
  • Guoyan Zheng
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Deakin UniversityGeelongAustralia
  3. 3.University of Western OntarioLondonCanada

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