Advertisement

Hierarchical Brain Parcellation with Uncertainty

  • Mark S. GrahamEmail author
  • Carole H. Sudre
  • Thomas Varsavsky
  • Petru-Daniel Tudosiu
  • Parashkev Nachev
  • Sebastien Ourselin
  • M. Jorge Cardoso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12443)

Abstract

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are ‘flat’. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree.

Supplementary material

505690_1_En_3_MOESM1_ESM.pdf (183 kb)
Supplementary material 1 (pdf 183 KB)

References

  1. 1.
    Whole-brain segmentation protocol. http://neuromorphometrics.com/Seg/
  2. 2.
    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
  3. 3.
    Demyanov, S., et al.: Tree-loss function for training neural networks on weakly-labelled datasets. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 287–291. IEEE (2017)Google Scholar
  4. 4.
    Eaton-Rosen, Z., Bragman, F., Bisdas, S., Ourselin, S., Cardoso, M.J.: Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 691–699. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00928-1_78CrossRefGoogle Scholar
  5. 5.
    Han, S., Carass, A., Prince, J.L.: Hierarchical parcellation of the cerebellum. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 484–491. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-32248-9_54CrossRefGoogle Scholar
  6. 6.
    Hu, H., Zhou, G.T., Deng, Z., Liao, Z., Mori, G.: Learning structured inference neural networks with label relations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2960–2968 (2016)Google Scholar
  7. 7.
    Isensee, F., Petersen, J., Kohl, S.A.A., Jäger, P.F., Maier-Hein, K.H.: nnU-net: breaking the spell on successful medical image segmentation (2019)Google Scholar
  8. 8.
    Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)Google Scholar
  9. 9.
    Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)Google Scholar
  10. 10.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  11. 11.
    Landman, B., Warfield, S.: MICCAI 2012 workshop on multi-atlas labeling. In: Medical Image Computing and Computer Assisted Intervention Conference (2012)Google Scholar
  12. 12.
    Liang, X., Zhou, H., Xing, E.: Dynamic-structured semantic propagation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 752–761 (2018)Google Scholar
  13. 13.
    Petersen, R.C., et al.: Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. Neurology 74(3), 201–209 (2010)CrossRefGoogle Scholar
  14. 14.
    Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)Google Scholar
  15. 15.
    Wang, G., Li, W., Vercauteren, T., Ourselin, S.: Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front. Comput. Neurosci. 13, 56 (2019)CrossRefGoogle Scholar
  16. 16.
    Wu, C., Tygert, M., LeCun, Y.: A hierarchical loss and its problems when classifying non-hierarchically. PLoS ONE 14(12), e0226222 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mark S. Graham
    • 1
    Email author
  • Carole H. Sudre
    • 1
  • Thomas Varsavsky
    • 1
  • Petru-Daniel Tudosiu
    • 1
  • Parashkev Nachev
    • 2
  • Sebastien Ourselin
    • 1
  • M. Jorge Cardoso
    • 1
  1. 1.Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Institute of NeurologyUniversity College LondonLondonUK

Personalised recommendations