Brain Tumor Segmentation and Parsing on MRIs Using Multiresolution Neural Networks

  • Laura Silvana CastilloEmail author
  • Laura Alexandra DazaEmail author
  • Luis Carlos RiveraEmail author
  • Pablo Arbeláez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)


Brain lesion segmentation is a critical application of computer vision to the biomedical image analysis. The difficulty is derived from the great variance between instances, and the high computational cost of processing three dimensional data. We introduce a neural network for brain tumor semantic segmentation that parses their internal structures and is capable of processing volumetric data from multiple MRI modalities simultaneously. As a result, the method is able to learn from small training datasets. We develop an architecture that has four parallel pathways with residual connections. It receives patches from images with different spatial resolutions and analyzes them independently. The results are then combined using fully-connected layers to obtain a semantic segmentation of the brain tumor. We evaluated our method using the 2017 BraTS Challenge dataset, reaching average dice coefficients of \(89\%\), \(88\%\) and \(86\%\) over the training, validation and test images, respectively.


Semantic segmentation Brain tumors Machine learning Deep learning MRI 


  1. 1.
    Mayo Clinic: Brain tumor - Symptoms and causes. Accessed 19 July 2017
  2. 2. Brain tumours - tumours of the central nervous system. Accessed 24 Aug 2017
  3. 3.
    The Royal Marsden NHS Foundation Trust: Glioma. Accessed 24 Aug 2017
  4. 4.
    Menze, B., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B., Ayache, N., Buendia, P., Collins, L., Cordier, N., Corso, J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K., Jena, R., John, N., Konukoglu, E., Lashkari, D., Antonio Mariz, J., Meier, R., Pereira, S., Precup, D., Price, S.J., Riklin-Raviv, T., Reza, S., Ryan, M., Schwartz, L., Shin, H.C., Shotton, J., Silva, C., Sousa, N., Subbanna, N., Szekely, G., Taylor, T., Thomas, O., Tustison, N., Unal, G., Vasseur, F., Wintermark, M., Hye Ye, D., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  5. 5.
    Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)CrossRefGoogle Scholar
  6. 6.
    Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  7. 7.
    Bauer, S., Fejes, T., Slotboom, J., Wiest, R., Nolte, L.P., Reyes, M.: Segmentation of brain tumor images based on integrated hierarchical classification and regularization. In: Proceedings MICCAI-BRATS (2012)Google Scholar
  8. 8.
    Menze, B.H., Geremia, E., Ayache, N., Szekely, G.: Segmenting glioma in multi-modal images using a generative-discriminative model for brain lesion segmentation. In: Proceedings MICCAI-BRATS (2012)Google Scholar
  9. 9.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). Google Scholar
  10. 10.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. CoRR abs/1605.06211 (2016)Google Scholar
  11. 11.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015)Google Scholar
  12. 12.
    Milletari, F., Navab, N., Ahmadi, S.: V-net: fully convolutional neural networks for volumetric medical image segmentation. CoRR abs/1606.04797 (2016)Google Scholar
  13. 13.
    Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A., Criminisi, A., Rueckert, D., Glocker, B.: Deepmedic on brain tumor segmentation. In: Proceedings of BRATS-MICCAI code, Athens, Greece.
  14. 14.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  15. 15.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017).
  16. 16.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-IGG collection. The Cancer Imaging Archive (2017).
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  18. 18.
    Rote, G.: Computing the minimum hausdorff distance between two point sets on a line under translation. Inf. Process. Lett. 38(3), 123–127 (1991)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringUniversidad de los AndesBogotáColombia

Personalised recommendations