Convolutional Neural Network Based Segmentation of Demyelinating Plaques in MRI

  • Bartłomiej Stasiak
  • Paweł Tarasiuk
  • Izabela Michalska
  • Arkadiusz TomczykEmail author
  • Piotr S. Szczepaniak
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)


In this paper a new architecture of convolutional neural networks is proposed. It is a fully-convolutional architecture which allows to keep the size of the processed image constant. This, in consequence, allows to apply it for image segmentation tasks where for a given image a mask representing sought regions should be produced. An additional advantage of this architecture is its ability to learn from smaller images which reduces the amount of data that must be propagated through the network. The trained network can be still applied to images of any size. The proposed method was used for automatic localization of demyelinating plaques in head MRI sequences. This work was possible, which should be emphasized, only thanks to the manually outlined plaques provided by radiologist. To present characteristic of the considered approach three architectures and three result evaluation methods were discussed and compared.


Multiple sclerosis Segmentation Machine learning Convolutional neural networks 



This project has been partly funded with support from National Science Centre, Republic of Poland, decision number DEC-2012/05/D/ST6/03091.

Authors would like to express their gratitude to the Department of Radiology of Barlicki University Hospital in Lodz for making head MRI sequences available.


  1. 1.
    Tomczyk, A., Spurek, P., Podgórski, M., Misztal, K., Tabor, J.: Detection of elongated structures with hierarchical active partitions and CEC-based image representation. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. AISC, vol. 403, pp. 159–168. Springer, Cham (2016). Scholar
  2. 2.
    Tomczyk, A., Szczepaniak, P.S.: Adaptive potential active contours. Pattern Anal. Appl. 14, 425–440 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    de Brebisson, A., Montana, G.: Deep Neural Networks for Anatomical Brain Segmentation. ArXiv e-prints arXiv:1502.02445 (2015)
  4. 4.
    Shelhamer, E., Long, J., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. ArXiv e-prints arXiv:1605.06211 (2016)
  5. 5.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. ArXiv e-prints arXiv:1606.04797 (2016)
  6. 6.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv e-prints arXiv:1505.04597 (2015)Google Scholar
  7. 7.
    Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J. Neurophysiol. 28, 229–289 (1965)CrossRefGoogle Scholar
  8. 8.
    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. (2012)Google Scholar
  9. 9.
    LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge (1995)Google Scholar
  10. 10.
    Cireşan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 2011, vol. 2, pp. 1237–1242. AAAI Press (2011)Google Scholar
  11. 11.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)Google Scholar
  12. 12.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013)Google Scholar
  13. 13.
    Nguyen, T.V., Lu, C., Sepulveda, J., Yan, S.: Adaptive nonparametric image parsing. CoRR abs/1505.01560 (2015)CrossRefGoogle Scholar
  14. 14.
    Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7405–7415 (2016)CrossRefGoogle Scholar
  15. 15.
    Mopuri, K.R., Babu, R.V.: Object level deep feature pooling for compact image representation. CoRR abs/1504.06591 (2015)Google Scholar
  16. 16.
    Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16, 555–559 (2003)CrossRefGoogle Scholar
  17. 17.
    Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. CoRR abs/1412.1283 (2014)Google Scholar
  18. 18.
    Stasiak, B., Tarasiuk, P., Michalska, I., Tomczyk, A., Szczepaniak, P.: Localization of demyelinating plaques in MRI using convolutional neural networks. In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), BIOIMAGING, vol. 2, pp. 55–64. SCITEPRESS (2017)Google Scholar
  19. 19.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. CoRR abs/1502.01852 (2015)Google Scholar
  20. 20.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp. 2278–2324 (1998)CrossRefGoogle Scholar
  21. 21.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bartłomiej Stasiak
    • 1
  • Paweł Tarasiuk
    • 1
  • Izabela Michalska
    • 2
  • Arkadiusz Tomczyk
    • 1
    Email author
  • Piotr S. Szczepaniak
    • 1
  1. 1.Institute of Information TechnologyLodz University of TechnologyLodzPoland
  2. 2.Department of RadiologyBarlicki University HospitalLodzPoland

Personalised recommendations