Lifted Auto-Context Forests for Brain Tumour Segmentation

  • Loic Le FolgocEmail author
  • Aditya V. Nori
  • Siddharth Ancha
  • Antonio Criminisi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


We revisit Auto-Context Forests for brain tumour segmentation in multi-channel magnetic resonance images, where semantic context is progressively built and refined via successive layers of Decision Forests (DFs). Specifically, we make the following contributions: (1) improved generalization via an efficient node-splitting criterion based on hold-out estimates, (2) increased compactness at a tree-level, thereby yielding shallow discriminative ensembles trained orders of magnitude faster, and (3) guided semantic bagging that exposes latent data-space semantics captured by forest pathways. The proposed framework is practical: the per-layer training is fast, modular and robust. It was a top performer in the MICCAI 2016 BRATS (Brain Tumour Segmentation) challenge, and this paper aims to discuss and provide details about the challenge entry.



The authors would like to thank the Microsoft–Inria Joint Centre for partially funding this work.


  1. 1.
    Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  2. 2.
    Archambeau, C., Verleysen, M.: Robust Bayesian clustering. Neural Netw. 20(1), 129–138 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  4. 4.
    Cordier, N., Delingette, H., Ayache, N.: A patch-based approach for the segmentation of pathologies: application to glioma labelling. IEEE Trans. Med. Imaging 35(4), 1066–1076 (2015)CrossRefGoogle Scholar
  5. 5.
    Criminisi, A., Robertson, D., Konukoglu, E., Shotton, J., Pathak, S., White, S., Siddiqui, K.: Regression forests for efficient anatomy detection and localization in computed tomography scans. Med. Image Anal. 17(8), 1293–1303 (2013)CrossRefGoogle Scholar
  6. 6.
    Geremia, E., Menze, B.H., Ayache, N.: Spatially adaptive random forests. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 1344–1347. IEEE (2013)Google Scholar
  7. 7.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  8. 8.
    Kamnitsas, K., Ledig, C., Newcombe, V.F.J., 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). ElsevierCrossRefGoogle Scholar
  9. 9.
    Menze, B., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  10. 10.
    Menze, B.H., Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 151–159. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15745-5_19 CrossRefGoogle Scholar
  11. 11.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_12 CrossRefGoogle Scholar
  12. 12.
    Quinlan, J.R.: Simplifying decision trees. Int. J. Man. Mach. Stud. 27(3), 221–234 (1987)CrossRefGoogle Scholar
  13. 13.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)Google Scholar
  14. 14.
    Tu, Z.: Auto-context and its application to high-level vision tasks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)Google Scholar
  15. 15.
    Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2010)CrossRefGoogle Scholar
  16. 16.
    Tustison, N., Gee, J.: N4ITK: Nicks N3 ITK implementation for MRI bias field correction. Insight J. (2009).
  17. 17.
    Tustison, N.J., Shrinidhi, K., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13(2), 209–225 (2015)CrossRefGoogle Scholar
  18. 18.
    Tustison, N., Wintermark, M., Durst, C., Avants, B.: Ants and arboles. Multimodal Brain Tumor Segmentation, p. 47 (2013)Google Scholar
  19. 19.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. I-511. IEEE (2001)Google Scholar
  20. 20.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov Random Field model and the Expectation-Maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar
  21. 21.
    Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33454-2_46 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Loic Le Folgoc
    • 1
    Email author
  • Aditya V. Nori
    • 1
  • Siddharth Ancha
    • 1
  • Antonio Criminisi
    • 1
  1. 1.Microsoft Research CambridgeCambridgeUK

Personalised recommendations