Advertisement

Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation

  • Yanwu Xu
  • Mingming Gong
  • Huan Fu
  • Dacheng Tao
  • Kun Zhang
  • Kayhan BatmanghelichEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

The brain tumor segmentation task aims to classify sub-regions into peritumoral edema, necrotic core, enhancing and non-enhancing tumor core using multimodal MRI scans. This task is very challenging due to its intrinsic high heterogeneity of appearance and shape. Recently, with the development of deep models and computing resources, deep convolutional neural networks have shown their effectiveness on brain tumor segmentation from 3D MRI cans, obtaining the top performance in the MICCAI BraTS challenge 2017. In this paper we further boost the performance of brain tumor segmentation by proposing a multi-scale masked 3D U-Net which captures multi-scale information by stacking multi-scale images as inputs and incorporating a 3-D Atrous Spatial Pyramid Pooling (ASPP) layer. To filter noisy results for tumor core (TC) and enhancing tumor (ET), we train the TC and ET segmentation networks from the bounding box for whole tumor (WT) and TC, respectively. On the BraTS 2018 validation set, our method achieved average Dice scores of 0.8094, 0.9034, 0.8319 for ET, WT and TC, respectively. On the BraTS 2018 test set, our method achieved 0.7690, 0.8711, and 0.7792 dice scores for ET, WT and TC, respectively. Especially, our multi-scale masked 3D network achieved very promising results enhancing tumor (ET), which is hardest to segment due to small scales and irregular shapes.

Keywords

Brain tumor segmentation Multi-scale ASPP U-Net 

Supplementary material

References

  1. 1.
    Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. ArXiv e-prints, November 2018Google Scholar
  2. 2.
    Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The Cancer Imaging Archive 2017.  https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
  5. 5.
    Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. CoRR abs/1606.00915 (2016). http://arxiv.org/abs/1606.00915
  6. 6.
    Fidon, L., et al.: Generalised Wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. CoRR abs/1707.00478 (2017)Google Scholar
  7. 7.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Society for Artificial Intelligence and Statistics (2010)Google Scholar
  8. 8.
    Havaei, M., et al.: Brain tumor segmentation with deep neural networks. CoRR abs/1505.03540 (2015)Google Scholar
  9. 9.
    Isensee, F., Jaeger, P., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. CoRR abs/1707.00587 (2017)Google Scholar
  10. 10.
    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. CoRR abs/1802.10508 (2018)Google Scholar
  11. 11.
    Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. CoRR abs/1711.01468 (2017)Google Scholar
  12. 12.
    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). https://doi.org/10.1016/j.media.2016.10.004CrossRefGoogle Scholar
  13. 13.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)Google Scholar
  14. 14.
    Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: hybrid densely connected UNet for liver and tumor segmentation from ct volumes. IEEE Trans. Med. Imaging (2018)Google Scholar
  15. 15.
    Liu, J., Li, M., Wang, J., Wu, F., Liu, T., Pan, Y.: A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci. Technol. 19(6), 578–595 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  17. 17.
    Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)Google Scholar
  18. 18.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015). http://arxiv.org/abs/1505.04597Google Scholar
  19. 19.
    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
  20. 20.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010). http://dblp.uni-trier.de/db/journals/tmi/tmi29.html#TustisonACZEYG10CrossRefGoogle Scholar
  21. 21.
    Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. CoRR abs/1709.00382 (2017). http://arxiv.org/abs/1709.00382

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanwu Xu
    • 1
  • Mingming Gong
    • 1
    • 3
  • Huan Fu
    • 2
  • Dacheng Tao
    • 2
  • Kun Zhang
    • 3
  • Kayhan Batmanghelich
    • 1
    Email author
  1. 1.Department of Biomedical InformaticsUniversity of PittsburghPittsburghUSA
  2. 2.UBTECH Sydney AI Centre, SIT, FEITThe University of SydneySydneyAustralia
  3. 3.Philosophy DepartmentCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations