Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

  • Simon Andermatt
  • Simon Pezold
  • Philippe Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10008)


We present a supervised deep learning method to automatically segment 3D volumes of biomedical image data. The presented method takes advantage of a neural network with the main layers consisting of multi-dimensional gated recurrent units. We apply an on-the-fly data augmentation technique which allows for accurate estimations without the need for either a huge amount of training data or advanced data pre- or postprocessing. We show that our method performs amongst the leading techniques on a popular brain segmentation challenge dataset in terms of speed, accuracy and memory efficiency. We describe in detail advantages over a similar method which uses the well-established long short-term memory.


Deep learning GRU Multi-dimensional RNN Segmentation 


  1. 1.
    Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: cuDNN: Efficient Primitives for Deep Learning. arXiv:1410.0759 [cs], October 2014
  2. 2.
    Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:1406.1078 [cs, stat], June 2014
  3. 3.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv:1412.3555 [cs], December 2014
  4. 4.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv:1606.06650 [cs], June 2016
  5. 5.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: A Search Space Odyssey. arXiv:1503.04069 [cs], March 2015
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 (2014)
  8. 8.
    Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Proceedings of The 32nd International Conference on Machine Learning, pp. 2342–2350 (2015)Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc, Red Hook (2012)Google Scholar
  10. 10.
    Mendrik, A.M., Vincken, K.L., Kuijf, H.J., et al.: MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Comput. Intell. Neurosci. 2015, 16 (2015). doi: 10.1155/2015/813696. Article ID 813696CrossRefGoogle Scholar
  11. 11.
    Olah, C.: Understanding LSTM Networks, August 2015.
  12. 12.
    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, Heidelberg (2015)Google Scholar
  13. 13.
    Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 2998–3006. Curran Associates Inc., Red Hook (2015)Google Scholar
  14. 14.
    Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: Regularization of neural networks using dropconnect. In: Dasgupta, S., McAllester, D. (eds.) Proceedings of the 30th International Conference on Machine Learning (ICML-13), JMLR Workshop and Conference Proceedings, vol. 28, pp. 1058–1066, May 2013Google Scholar
  15. 15.
    Zeiler, M.D.: ADADELTA: An Adaptive Learning Rate Method. arXiv:1212.5701 [cs], December 2012

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Simon Andermatt
    • 1
  • Simon Pezold
    • 1
  • Philippe Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselAllschwilSwitzerland

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