On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

  • Wenqi Li
  • Guotai Wang
  • Lucas Fidon
  • Sebastien Ourselin
  • M. Jorge Cardoso
  • Tom Vercauteren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10265)

Abstract

Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.

References

  1. 1.
    Cardoso, M.J., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., Ourselin, S.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015)CrossRefGoogle Scholar
  2. 2.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs (2016). arXiv:1606.00915
  3. 3.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_49 CrossRefGoogle Scholar
  4. 4.
    Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V.C., Shi, L., Heng, P.A.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182–1195 (2016)CrossRefGoogle Scholar
  5. 5.
    Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: ICML (2016)Google Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV (2015)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). doi:10.1007/978-3-319-46493-0_38 CrossRefGoogle Scholar
  9. 9.
    Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? (2016). arXiv:1608.08614
  10. 10.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)Google Scholar
  11. 11.
    Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)CrossRefGoogle Scholar
  12. 12.
    Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv:1412.6980
  13. 13.
    Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., Biller, A.: Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 129 (2016)Google Scholar
  14. 14.
    Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: NIPS (2016)Google Scholar
  15. 15.
    Merkow, J., Marsden, A., Kriegman, D., Tu, Z.: Dense volume-to-volume vascular boundary detection. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 371–379. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_43 CrossRefGoogle Scholar
  16. 16.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision (2016)Google Scholar
  17. 17.
    Nyúl, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143–150 (2000)CrossRefGoogle Scholar
  18. 18.
    Shi, W., Zhuang, X., Wolz, R., Simon, D., Tung, K., Wang, H., Ourselin, S., Edwards, P., Razavi, R., Rueckert, D.: A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation. In: International Workshop on Statistical Atlases and Computational Models of the Heart (2011)Google Scholar
  19. 19.
    Veit, A., Wilber, M., Belongie, S.: Residual networks are exponential ensembles of relatively shallow networks. In: NIPS (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wenqi Li
    • 1
  • Guotai Wang
    • 1
  • Lucas Fidon
    • 1
  • Sebastien Ourselin
    • 1
  • M. Jorge Cardoso
    • 1
  • Tom Vercauteren
    • 1
  1. 1.Translational Imaging Group, Centre for Medical Image Computing (CMIC)University College LondonLondonUK

Personalised recommendations