Advertisement

Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Function

  • Xiaobin HuEmail author
  • Hongwei Li
  • Yu Zhao
  • Chao Dong
  • Bjoern H. Menze
  • Marie Piraud
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

For many segmentation tasks, especially for the biomedical image, the topological prior is vital information which is useful to exploit. The containment/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes ‘whole tumor’, ‘tumor core’, ‘active tumor’, the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network comprises a context aggregation pathway and a localization pathway, which encodes increasingly abstract representation of the input as going deeper into the network, and then recombines these representations with shallower features to precisely localize the interest domain via a localization path. The nested-class-prior is combined by proposing the multi-class activation function and its corresponding loss function. The model is trained on the training dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing tumor and tumor core classes without relying on ensembles or complicated post-processing steps. Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.

Keywords

Topological prior Nested classes 3D-residual-Unet Multi-class activation function 

References

  1. 1.
    Davis, M.E.: Glioblastoma: overview of disease and treatment. Clin. J. Oncol. Nurs. 20(5), S2–S8 (2016).  https://doi.org/10.1188/16.CJON.S1.2-8CrossRefGoogle Scholar
  2. 2.
    Hanif, F., Muzaffar, K., Perveen, K., Malhi, S.M., Simjee, S.U.: Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pac. J. Cancer Prev. 18, 3–9 (2017)Google Scholar
  3. 3.
    Birbrair, A., et al.: Novel peripherally derived neural-like stem cells as therapeutic carriers for treating glioblastomas. STEM CELLS Transl. Med. 6, 471–481 (2017)CrossRefGoogle Scholar
  4. 4.
    Gu, J.X., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)CrossRefGoogle Scholar
  5. 5.
    Nosrati, M.S., Hamarneh, G.: Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints. IEEE Trans. Med. Imaging 33, 1845–1859 (2014)CrossRefGoogle Scholar
  6. 6.
    BenTaieb, A., Hamarneh, G.: Topology aware fully convolutional networks for histology gland segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 460–468. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_53CrossRefGoogle Scholar
  7. 7.
    Christ, P.F., et al.: Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 415–423. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_48CrossRefGoogle Scholar
  8. 8.
    Fidon, L., et al.: Generalised Wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 64–76. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75238-9_6CrossRefGoogle Scholar
  9. 9.
    Bauer, S., Tessier, J., Krieter, O., Nolte, L.-P., Reyes, M.: Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds.) MCV 2013. LNCS, vol. 8331, pp. 74–83. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-05530-5_8CrossRefGoogle Scholar
  10. 10.
    Alberts, E., et al.: A nonparametric growth model for brain tumor segmentation in longitudinal MR sequences. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 69–79. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30858-6_7CrossRefGoogle Scholar
  11. 11.
    Liu, Z.W., Li, X.X., Luo, P., Loy, C.C., Tang, X.O.: Deep learning Markov random field for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1–1, 8828 (2017)Google Scholar
  12. 12.
    Piraud, M., Sekuboyina, A., Menze, B.H.: Multi-level activation for segmentation of hierarchically-nested classes. In: Computer Vision and Pattern Recognition Workshop (2018)Google Scholar
  13. 13.
    Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287–297. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75238-9_25CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  16. 16.
    Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  17. 17.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017).  https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
  18. 18.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive (2014).  https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
  19. 19.
    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)
  20. 20.
    Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427–436 (2017)Google Scholar
  21. 21.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision, pp. 565–571 (2016)Google Scholar
  22. 22.
    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
  23. 23.
    Crum, W.R., Camara, O., Hill, D.L.G.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging 25(11), 1451–1461 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiaobin Hu
    • 1
    Email author
  • Hongwei Li
    • 1
  • Yu Zhao
    • 1
  • Chao Dong
    • 1
  • Bjoern H. Menze
    • 1
  • Marie Piraud
    • 1
  1. 1.Department of Computer ScienceTechnische Universität MünchenMunichGermany

Personalised recommendations