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Deep Hourglass for Brain Tumor Segmentation

  • Eze BensonEmail author
  • Michael P. Pound
  • Andrew P. French
  • Aaron S. Jackson
  • Tony P. Pridmore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training. We train and evaluate a convolutional neural network (CNN) encoder-decoder network based on a singular hourglass structure. The hourglass network is able to classify the whole tumour (WT), enhancing (ET) tumour and core tumour (TC) in one pass. We apply a small amount of preprocessing to the data before feeding it to the network but no post processing. We apply our method to two different unseen sets of volumes containing 66 and 191 volumes. We achieve an overall Dice coefficient of 92% on the training set. On the first unseen set our network achieves Dice coefficients of 0.66, 0.82 and 0.72 for ET, WT and TC. On the second unseen set our network achieves Dice coefficients of 0.62, 0.79 and 0.65 on ET, WT and TC.

Keywords

Convolutional neural network Deep learning Hourglass Glioma 

References

  1. 1.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  2. 2.
    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
  3. 3.
    Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46484-8_29CrossRefGoogle Scholar
  4. 4.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)CrossRefGoogle Scholar
  5. 5.
    Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. data. 5(4), 170117 (2017)CrossRefGoogle Scholar
  6. 6.
    Bakas, et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive, p. 286 (2017)Google Scholar
  7. 7.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive, p. 286 (2017)Google Scholar
  8. 8.
    Bakas, S., Reyes, M., Menze, B.: 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)
  9. 9.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  10. 10.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)Google Scholar
  11. 11.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561, 2 November 2015
  12. 12.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  13. 13.
    Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: The IEEE International Conference on Computer Vision (ICCV), vol. 1, no. 2, p. 4, 1 October 2017Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eze Benson
    • 1
    Email author
  • Michael P. Pound
    • 1
  • Andrew P. French
    • 1
    • 2
  • Aaron S. Jackson
    • 1
  • Tony P. Pridmore
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.School of BiosciencesUniversity of NottinghamNottinghamUK

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