Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU

  • Antal HorváthEmail author
  • Charidimos Tsagkas
  • Simon Andermatt
  • Simon Pezold
  • Katrin Parmar
  • Philippe Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11397)


The small butterfly shaped structure of spinal cord (SC) gray matter (GM) is challenging to image and to delineate from its surrounding white matter (WM). Segmenting GM is up to a point a trade-off between accuracy and precision. We propose a new pipeline for GM-WM magnetic resonance (MR) image acquisition and segmentation. We report superior results as compared to the ones recently reported in the SC GM segmentation challenge and show even better results using the averaged magnetization inversion recovery acquisitions (AMIRA) sequence. Scan-rescan experiments with the AMIRA sequence show high reproducibility in terms of Dice coefficient, Hausdorff distance and relative standard deviation. We use a recurrent neural network (RNN) with multi-dimensional gated recurrent units (MD-GRU) to train segmentation models on the AMIRA dataset of 855 slices. We added a generalized dice loss to the cross entropy loss that MD-GRU uses and were able to improve the results.


Segmentation Spinal cord Gray matter White matter Deep learning RNN MD-GRU 



We thank Dr. Matthias Weigel, Prof. Dr. Oliver Bieri and Tanja Haas for the MR acquisitions with the AMIRA sequence.


  1. 1.
    Andermatt, S., Pezold, S., Cattin, P.: Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data. In: Carneiro, G., et al. (eds.) LABELS/DLMIA-2016. LNCS, vol. 10008, pp. 142–151. Springer, Cham (2016). Scholar
  2. 2.
    Crum, W.R., Camara, O., Hill, D.L.G.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451–1461 (2006)CrossRefGoogle Scholar
  3. 3.
    Datta, E., Papinutto, N., Schlaeger, R., Zhu, A., Carballido-Gamio, J., Henry, R.G.: Gray matter segmentation of the spinal cord with active contours in MR images. NeuroImage 147, 788–799 (2017)CrossRefGoogle Scholar
  4. 4.
    Horváth, A., et al.: A principled approach to combining inversion recovery images. In: Proceedings of the 26th Annual Meeting of ISMRM, Paris, France, June 2018Google Scholar
  5. 5.
    Perone, C.S., Calabrese, E., Cohen-Adad, J.: Spinal cord gray matter segmentation using deep dilated convolutions. Sci. Rep. 8(1), 5966 (2018)CrossRefGoogle Scholar
  6. 6.
    Porisky, A., et al.: Grey matter segmentation in spinal cord MRIs via 3D convolutional encoder networks with shortcut connections. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS-2017. LNCS, vol. 10553, pp. 330–337. Springer, Cham (2017). Scholar
  7. 7.
    Prados, F., et al.: Spinal cord grey matter segmentation challenge. NeuroImage 152, 312–329 (2017)CrossRefGoogle Scholar
  8. 8.
    Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS-2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). Scholar
  9. 9.
    Weigel, M., Bieri, O.: Spinal cord imaging using averaged magnetization inversion recovery acquisitions. Magn. Reson. Med. 79(4), 1870–1881 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antal Horváth
    • 1
    Email author
  • Charidimos Tsagkas
    • 2
  • Simon Andermatt
    • 1
  • Simon Pezold
    • 1
  • Katrin Parmar
    • 2
  • Philippe Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselAllschwilSwitzerland
  2. 2.Department of NeurologyUniversity Hospital BaselBaselSwitzerland

Personalised recommendations