Advertisement

Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation

  • Haocheng Shen
  • Ruixuan Wang
  • Jianguo Zhang
  • Stephen J. McKenna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

We propose a novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor. This network extracts multi-level contextual information by concatenating hierarchical feature representations extracted from multimodal MR images along with their symmetric-difference images. It achieves improved segmentation performance by incorporating boundary information directly into the loss function. The proposed method was evaluated on the BRATS13 and BRATS15 datasets and compared with competing methods on the BRATS13 testing set. Segmented tumor boundaries obtained were better than those obtained by single-task FCN and by FCN with CRF. The method is among the most accurate available and has relatively low computational cost at test time.

Keywords

Deep learning Tumor segmentation Multi-task learning 

Notes

Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (No. 61628212).

References

  1. 1.
    Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  2. 2.
    Tustison, N.J., Shrinidhi, K.L., Wintermark, M., et al.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209–225 (2015)CrossRefGoogle Scholar
  3. 3.
    Pereira, S., Pinto, A., Alves, V., et al.: Brain tumor segmentation using convolutional neural networks in MRI images. Med. Imaging 35(5), 1240–1251 (2016)CrossRefGoogle Scholar
  4. 4.
    Havaei, M., Davy, A., Warde-Farley, D., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  5. 5.
    Kamnitsas, K., Ledig, C., Newcombe, V.F., 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.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS, pp. 109–117 (2011)Google Scholar
  7. 7.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  8. 8.
    Shen, H., Zhang, J., Zheng, W.: Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. In: ICIP (2017, to appear)Google Scholar
  9. 9.
    Kwon, D., Shinohara, R.T., Akbari, H., Davatzikos, C.: Combining generative models for multifocal glioma segmentation and registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 763–770. Springer, Cham (2014). doi: 10.1007/978-3-319-10404-1_95 Google Scholar
  10. 10.
    Zhao, X., Wu, Y., Song, G., et al.: Brain tumor segmentation using a fully convolutional neural network with conditional random fields. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, pp. 75–87. Springer, Cham (2016). doi: 10.1007/978-3-319-55524-9_8 CrossRefGoogle Scholar
  11. 11.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)Google Scholar
  12. 12.
    Chen, L.C., Papandreou, G., Kokkinos, I., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)
  13. 13.
    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). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  14. 14.
    Chen, H., Qi, X.J., Cheng, J.Z., Heng, P.A.: Deep contextual networks for neuronal structure segmentation. In: AAAI (2016)Google Scholar
  15. 15.
    Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: CVPR, pp. 2487–2496 (2016)Google Scholar
  16. 16.
    Xu, Y., Li, Y., Liu, M., Wang, Y., Lai, M., Chang, E.I.-C.: Gland instance segmentation by deep multichannel side supervision. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 496–504. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_57 CrossRefGoogle Scholar
  17. 17.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV, pp. 1026–1034 (2015)Google Scholar
  18. 18.
    Zheng, S., Jayasumana, S., Romera-Paredes, B., et al.: Conditional random fields as recurrent neural networks. In: ICCV, pp. 1529–1537 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haocheng Shen
    • 1
  • Ruixuan Wang
    • 1
  • Jianguo Zhang
    • 1
  • Stephen J. McKenna
    • 1
  1. 1.Computing, School of Science and EngineeringUniversity of DundeeDundeeUK

Personalised recommendations