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Learning Contextual and Attentive Information for Brain Tumor Segmentation

  • Chenhong Zhou
  • Shengcong Chen
  • Changxing DingEmail author
  • Dacheng Tao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Thanks to the powerful representation learning ability, convolutional neural network has been an effective tool for the brain tumor segmentation task. In this work, we design multiple deep architectures of varied structures to learning contextual and attentive information, then ensemble the predictions of these models to obtain more robust segmentation results. In this way, the risk of overfitting in segmentation is reduced. Experimental results on validation dataset of BraTS 2018 challenge demonstrate that the proposed method can achieve good performance with average Dice scores of 0.8136, 0.9095 and 0.8651 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding scores for BraTS 2018 testing set are 0.7775, 0.8842 and 0.7960, respectively, winning the third position in the BraTS 2018 competition among 64 participating teams.

Notes

Acknowledgments

Changxing Ding was supported in part by the National Natural Science Foundation of China (Grant No.: 61702193), Science and Technology Program of Guangzhou (Grant No.: 201804010272), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No.: 2017ZT07X183). Dacheng Tao was supported by Australian Research Council Projects (FL-170100117, DP-180103424 and LP-150100671).

References

  1. 1.
    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
  2. 2.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE TMI 34(10), 1993–2024 (2015)Google Scholar
  3. 3.
    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, R97–R129 (2013)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  6. 6.
    Shen, H., Wang, R., Zhang, J., McKenna, S.J.: Boundary-aware fully convolutional network for brain tumor segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 433–441. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_49CrossRefGoogle Scholar
  7. 7.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. In: The Cancer Imaging Archive (2017)Google Scholar
  8. 8.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. In: The Cancer Imaging Archive (2017)Google Scholar
  9. 9.
    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
  10. 10.
    Quan, T.M., et al.: Fusionnet: a deep fully residual convolutional neural network for image segmentation in connectomics. arXiv preprint arXiv:1612.05360 (2016)
  11. 11.
    Zhao, X., Wu, Y., Song, G., et al.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)CrossRefGoogle Scholar
  12. 12.
    Bengio, Y., Louradour, J., Collobert, R., and Weston, J.: Curriculum learning. In: ICML, pp. 41–48. ACM (2009)Google Scholar
  13. 13.
    Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637–645. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00931-1_73CrossRefGoogle Scholar
  15. 15.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imag. 35(5), 1240–1251 (2016)CrossRefGoogle Scholar
  16. 16.
    Hu, J., Shen, L., and Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2016)
  17. 17.
    Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J.: UNet++: A nested U-Net architecture for medical image segmentation. arXiv preprint arXiv:1807.10165 (2018)
  18. 18.
    Long, J., et al.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 343–3440 (2015)Google Scholar
  19. 19.
    Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75238-9_38CrossRefGoogle Scholar
  20. 20.
    Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75238-9_16CrossRefGoogle Scholar
  21. 21.
    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)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chenhong Zhou
    • 1
  • Shengcong Chen
    • 1
  • Changxing Ding
    • 1
    Email author
  • Dacheng Tao
    • 2
  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.UBTECH Sydney AI Centre, SIT, FEITUniversity of SydneySydneyAustralia

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