Automatic Segmentation of Lumbar Spine MRI Using Ensemble of 2D Algorithms

  • Nedelcho GeorgievEmail author
  • Asen AsenovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11397)


MRI is considered the gold standard in soft tissue diagnostic of the lumbar spine. Number of protocols and modalities are used – from one hand 2D sagittal, 2D angulated axial, 2D consecutive axial and 3D image types; from the other hand different sequences and contrasts are used: T1w, T2w; fat suppression, water suppression etc. Images of different modalities are not always aligned. Resolutions and field of view also vary. SNR is also different for different MRI equipment. So the goal should be to create an algorithm that covers great variety of imaging techniques.


  1. 1.
    Le Cun, Y., Bottou, L., Bengio, Y.: Reading checks with multilayer graph transformer networks. In: ICASSP 1997, vol. 1, pp. 151–154. IEEE (1997)Google Scholar
  2. 2.
    He, K., et al.: Deep residual learning for image recognition. In: CVPR 2016 (2016)Google Scholar
  3. 3.
    Hinton, G.E., et al.: Improving neural networks by preventing co-adaptation of feature detectors. Technical report. arXiv:1207.0580
  4. 4.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015)Google Scholar
  5. 5.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. arXiv:1411.4038 (2014)
  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). Scholar
  7. 7.
    Chen, C., Belavy, D., Zheng, G.: 3D intervertebral disc localization and segmentation from MR images by data-driven regression and classification. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 50–58. Springer, Cham (2014). Scholar
  8. 8.
    Xiaomeng, L., et al.: 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images. Med. Image Anal. 45, 41–54 (2018)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SmartSoft Ltd.VarnaBulgaria

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