3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures

  • Adrià CasamitjanaEmail author
  • Santi Puch
  • Asier Aduriz
  • Verónica Vilaplana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images. We address the problem using three different architectures that combine fine and coarse features to obtain the final segmentation. We compare three different networks that use multi-resolution features in terms of both design and performance and we show that they improve their single-resolution counterparts.


Receptive Field Convolutional Neural Network Final Segmentation Memory Constraint Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Menze, B.H., Jakab, A., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  2. 2.
    Kamnitsas, K., Ledig, C., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36, 61–78 (2017)CrossRefGoogle Scholar
  3. 3.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_12 CrossRefGoogle Scholar
  4. 4.
    Havaei, M., Davy, A., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. (2016)Google Scholar
  5. 5.
    Maier, O., Wilms, M., Handels, H.: Image features for brain lesion segmentation using random forests. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 119–130. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_11 CrossRefGoogle Scholar
  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). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  7. 7.
    Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: ICCV, Santiago, Chile (2015)Google Scholar
  8. 8.
    Long, J., Shelharmer, E., Darrel, T.: Fully convolutional networks for semantic segmentation. In: CVPR, Boston, USA (2015)Google Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556v6 (2015)
  10. 10.
    He, K., et al.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  11. 11.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_49 CrossRefGoogle Scholar
  12. 12.
    Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33454-2_46 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Adrià Casamitjana
    • 1
    Email author
  • Santi Puch
    • 1
  • Asier Aduriz
    • 1
  • Verónica Vilaplana
    • 1
  1. 1.Signal Theory and Communications DepartmentUniversitat Politècnica de Catalunya. BarcelonaTechBarcelonaSpain

Personalised recommendations