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)


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)


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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

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