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Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes

  • Adel KermiEmail author
  • Issam Mahmoudi
  • Mohamed Tarek Khadir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Precise 3D computerized segmentation of brain tumors remains, until nowadays, a challenging process due to the variety of the possible shapes, locations and image intensities of various tumors types. This paper presents a fully automated and efficient brain tumor segmentation method based on 2D Deep Convolutional Neural Networks (DNNs) which automatically extracts the whole tumor and intra-tumor regions, including enhancing tumor, edema and necrosis, from pre-operative multimodal 3D-MRI. The network architecture was inspired by U-net and has been modified to increase brain tumor segmentation performance. Among applied modifications, Weighted Cross Entropy (WCE) and Generalized Dice Loss (GDL) were employed as a loss function to address the class imbalance problem in the brain tumor data. The proposed segmentation system has been tested and evaluated on both, BraTS’2018 training and validation datasets, which include a total of 351 multimodal MRI volumes of different patients with HGG and LGG tumors representing different shapes, giving promising and objective results close to manual segmentation performances obtained by experienced neuro-radiologists. On the challenge validation dataset, our system achieved a mean enhancing tumor, whole tumor, and tumor core dice score of 0.783, 0.868 and 0.805 respectively. Other quantitative and qualitative evaluations are presented and discussed along the paper.

Keywords

Brain tumor segmentation 3D-MRI Machine learning Deep learning Convolutional Neural Networks U-net BraTS’2018 challenge 

Notes

Acknowledgments

This work was in part financially supported by an Algerian research project (CNEPRU) funded by the Ministry of Higher Education and Scientific Research (Project title and number: “PERFORM”, B*04120140014). We would like to thank Bakas, S., Ph.D. and Postdoctoral researcher at SBIA of Perelman School of Medicine University of Pennsylvania – USA for providing the entire BraTS’2018 datasets employed in this study. We also gratefully acknowledge the support of CERIST, the Algerian Research Center for Scientific and Technical Information, for allowing us the use of “IBNBADIS” Cluster, without which we could not have performed these tests.

References

  1. 1.
    Dass, R., Priyanka, Devi, S.: Image segmentation techniques. Int. J. Electron. Commun. Technol. (IJECT) 3, 66–70 (2012)Google Scholar
  2. 2.
    Kermi, A., Andjouh, K., Zidane, F.: Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets. IET Image Process. 12, 1964–1971 (2018)CrossRefGoogle Scholar
  3. 3.
    Khotanlou, H., Colliot, O., Atif, J., Bloch, I.: 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst. 160, 1457–1473 (2009)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Prastawaa, M., Bullitt, E., Geriga, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13, 297–311 (2009)CrossRefGoogle Scholar
  5. 5.
    Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys. 35, 3–14 (2010)CrossRefGoogle Scholar
  6. 6.
    Despotovic, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods Med. 2015, 23 (2015)CrossRefGoogle Scholar
  7. 7.
    Wirtz, C.R., et al.: The benefit of neuronavigation for neurosurgery analyzed by its impact on glioblastoma surgery. Neurol. Res. 22, 354–360 (2000)CrossRefGoogle Scholar
  8. 8.
    Yanyun, L., Zhijian, S.: Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis. Chin. Med. J. 127, 462–468 (2014)Google Scholar
  9. 9.
    Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Havaei, M., et al.: Brain tumor segmentation with Deep Neural Networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  12. 12.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016)Google Scholar
  13. 13.
    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
  14. 14.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017)Google Scholar
  15. 15.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive (2017)Google Scholar
  16. 16.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)CrossRefGoogle Scholar
  17. 17.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  18. 18.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  19. 19.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, Max (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_38CrossRefGoogle Scholar
  20. 20.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034. IEEE Computer Society (2015)Google Scholar
  21. 21.
    Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67558-9_28CrossRefGoogle Scholar
  22. 22.
    Kermi, A., Mahmoudi, I., Khadir, M.T.: Brain tumor segmentation in multimodal 3D-MRI of BraTS’2018 datasets using Deep Convolutional Neural Networks. In: Pre-conference Proceedings of the 7th International MICCAI BraTS’2018 Challenge, Granada, Spain, pp. 252–263 (2018)Google Scholar
  23. 23.
    Bakas, S., 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)
  24. 24.
    Castillo, L.S., Daza, L.A., Rivera, L.C., Arbelàez, P.: Volumetric multimodality neural network for brain tumor segmentation. In: Proceedings of the 6th MICCAI BraTS Challenge, Quebec, Canada, pp. 34–41 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LMCS LaboratoryNational Higher School of Computer Sciences (ESI)Oued-Smar, El-HarrachAlgeria
  2. 2.LabGed Laboratory, Department of Computer SciencesUniversity Badji-Mokhtar of AnnabaAnnabaAlgeria

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