Brain Tumor Segmentation Using an Adversarial Network

  • Zeju Li
  • Yuanyuan WangEmail author
  • Jinhua YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)


Recently, the convolutional neural network (CNN) has been successfully applied to the task of brain tumor segmentation. However, the effectiveness of a CNN-based method is limited by the small receptive field, and the segmentation results don’t perform well in the spatial contiguity. Therefore, many attempts have been made to strengthen the spatial contiguity of the network output. In this paper, we proposed an adversarial training approach to train the CNN network. A discriminator network is trained along with a generator network which produces the synthetic segmentation results. The discriminator network is encouraged to discriminate the synthetic labels from the ground truth labels. Adversarial adjustments provided by the discriminator network are fed back to the generator network to help reduce the differences between the synthetic labels and the ground truth labels and reinforce the spatial contiguity with high-order loss terms. The presented method is evaluated on the Brats2017 training dataset. The experiment results demonstrate that the presented method could enhance the spatial contiguity of the segmentation results and improve the segmentation accuracy.


Brain tumor segmentation Adversarial network Deep learning 



This work was supported by the National Basic Research Program of China (2015CB755500), the National Natural Science Foundation of China (11474071).


  1. 1.
    Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  2. 2.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MR images. IEEE Trans. on Med. Imaging 35(5), 1240–1251 (2016)CrossRefGoogle Scholar
  3. 3.
    Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., Zhang, Y.: 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, vol. 10154, pp. 77–80. Springer, Cham (2016). Google Scholar
  4. 4.
    Havaei, M., Davy, A., Wardefarley, D., Biard, A., Courville, A., Bengio, Y., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)CrossRefGoogle Scholar
  5. 5.
    Li, Z., Wang, Y., Yu, J., Shi, Z., Guo, Y., Chen, L., et al.: Low grade glioma segmentation based on CNN with fully connected CRF. J. Healthc. Eng. 2017 (2017). Article No. 9283480
  6. 6.
    Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.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
  7. 7.
    Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv:1611.08408 (2016)
  8. 8.
    Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al.: Generative adversarial networks. In: NIPS, pp. 2672–2680 (2014)Google Scholar
  9. 9.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
  10. 10.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci Data 4 (2017). Article No.170117
  11. 11.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., at al.: Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017)Google Scholar
  12. 12.
    Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., at al.: Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection. The Cancer Imaging Archive (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina
  2. 2.Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiShanghaiChina

Personalised recommendations