Advertisement

Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRI

  • Shahab AslaniEmail author
  • Michael Dayan
  • Vittorio Murino
  • Diego Sona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

In this paper, we propose an automated segmentation approach based on a deep two-dimensional fully convolutional neural network to segment brain multiple sclerosis lesions from multimodal magnetic resonance images. The proposed model is made as a combination of two deep subnetworks. An encoding network extracts different feature maps at various resolutions. A decoding part upconvolves the feature maps combining them through shortcut connections during an upsampling procedure. To the best of our knowledge, the proposed model is the first slice-based fully convolutional neural network for the purpose of multiple sclerosis lesion segmentation. We evaluated our network on a freely available dataset from ISBI MS challenge with encouraging results from a clinical perspective.

Keywords

Segmentation Multiple sclerosis Convolutional neural network 

Notes

Acknowledgments

We respectfully acknowledge NVIDIA for GPU donation.

References

  1. 1.
    Brosch, T., Tang, L.Y., Yoo, Y., Li, D.K., Traboulsee, A., Tam, R.: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 35(5), 1229–1239 (2016)CrossRefGoogle Scholar
  2. 2.
    Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77–102 (2017)CrossRefGoogle Scholar
  3. 3.
    Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
  4. 4.
    García-Lorenzo, D., Francis, S., Narayanan, S., Arnold, D.L., Collins, D.L.: Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal. 17(1), 1–18 (2013)CrossRefGoogle Scholar
  5. 5.
    Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  7. 7.
    He, R., Narayana, P.A.: Automatic delineation of Gd enhancements on magnetic resonance images in multiple sclerosis. Med. Phys. 29(7), 1536–1546 (2002)CrossRefGoogle Scholar
  8. 8.
    Jesson, A., Arbel, T.: Hierarchical MRF and random forest segmentation of ms lesions and healthy tissues in brain MRI. In: The Longitudinal MS Lesion Segmentation Challenge (2015)Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  11. 11.
    Maier, O., Handels, H.: MS lesion segmentation in MRI with random forests. In: Proceedings of the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge, pp. 1–2 (2015)Google Scholar
  12. 12.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
  13. 13.
    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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  14. 14.
    Steinman, L.: Multiple sclerosis: a coordinated immunological attack against myelin in the central nervous system. Cell 85(3), 299–302 (1996)CrossRefGoogle Scholar
  15. 15.
    Valverde, S., et al.: Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage 155, 159–168 (2017)CrossRefGoogle Scholar
  16. 16.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shahab Aslani
    • 1
    • 2
    Email author
  • Michael Dayan
    • 1
  • Vittorio Murino
    • 1
    • 3
  • Diego Sona
    • 1
    • 4
  1. 1.Pattern Analysis and Computer Vision (PAVIS)Istituto Italiano di Tecnologia (IIT)GenoaItaly
  2. 2.Science and Technology for Electronic and Telecommunication EngineeringUniversity of GenoaGenoaItaly
  3. 3.Dipartimento di InformaticaUniversity of VeronaVeronaItaly
  4. 4.NeuroInformatics LaboratoryFondazione Bruno KesslerTrentoItaly

Personalised recommendations