Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure

  • Elodie PuybareauEmail author
  • Guillaume Tochon
  • Joseph Chazalon
  • Jonathan Fabrizio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


This paper proposes, in the context of brain tumor study, a fast automatic method that segments tumors and predicts patient overall survival. The segmentation stage is implemented using a fully convolutional network based on VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color-like images which contain 3D information of MRI volumes. With such a technique, the segmentation of a 3D volume takes only a few seconds. The prediction stage is implemented using Random Forests. It only requires a predicted segmentation of the tumor and a homemade atlas. Its simplicity allows to train it with very few examples and it can be used after any segmentation process. The presented method won the second place of the MICCAI 2018 BraTS Challenge for the overall survival prediction task. A Docker image is publicly available on


Glioma Tumor segmentation Survival prediction Fully convolutional network Random forest 



The authors would like to thank the organizers of the BraTS 2018 Challenge and the MICCAI Brainles Workshop, and Dr. Marie Donzel from Claude Bernard University Lyon 1 medical school for the useful discussions regarding the definition of relevant brain features for the survival prediction. The GPU card “Quadro P6000” used for the work presented in this paper was donated by NVIDIA Corporation.


  1. 1.
    Angelini, E.D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Curr. Med. Imaging Rev. 3(4), 262–276 (2007)CrossRefGoogle Scholar
  2. 2.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017).
  3. 3.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017).
  4. 4.
    Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  5. 5.
    Bakas, S., Reyes, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
  6. 6.
    Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)CrossRefGoogle Scholar
  7. 7.
    Bonnín Rosselló, C.: Brain lesion segmentation using Convolutional Neuronal Networks. B.S. thesis, Universitat Politècnica de Catalunya (2018)Google Scholar
  8. 8.
    Eisenhauer, E.A., et al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45(2), 228–247 (2009)CrossRefGoogle Scholar
  9. 9.
    Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683–688 (2001)CrossRefGoogle Scholar
  10. 10.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)Google Scholar
  11. 11.
    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
  12. 12.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  13. 13.
    Louis, D.N., et al.: The 2007 who classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007)CrossRefGoogle Scholar
  14. 14.
    Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). Scholar
  15. 15.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)CrossRefGoogle Scholar
  16. 16.
    Ohgaki, H., Kleihues, P.: Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J. Neuropathol. Exp. Neurol. 64(6), 479–489 (2005)CrossRefGoogle Scholar
  17. 17.
    Reddi, S.J., Kale, S., Kumar, S.: On the convergence of Adam and beyond. In: International Conference on Learning Representations (2018)Google Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  19. 19.
    Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8(1), 25 (2007)CrossRefGoogle Scholar
  20. 20.
    Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)CrossRefGoogle Scholar
  21. 21.
    Wen, P.Y., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28(11), 1963–1972 (2010)CrossRefGoogle Scholar
  22. 22.
    Xu, Y., Géraud, T., Bloch, I.: From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. In: Proceedings of the 23rd IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 4417–4421, September 2017Google Scholar
  23. 23.
    Xu, Y., Géraud, T., Najman, L.: Connected filtering on tree-based shape-spaces. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1126–1140 (2016)CrossRefGoogle Scholar
  24. 24.
    Xu, Y., Géraud, T., Puybareau, É., Bloch, I., Chazalon, J.: White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 501–514. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Elodie Puybareau
    • 1
    Email author
  • Guillaume Tochon
    • 1
  • Joseph Chazalon
    • 1
  • Jonathan Fabrizio
    • 1
  1. 1.EPITA Research and Development Laboratory (LRDE)Le Kremlin-BicêtreFrance

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