Skip to content

Hierarchical Brain Parcellation with Uncertainty

0Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Graham, M. S., Sudre, C. H., Varsavsky, T., Tudosiu, P. D., Nachev, P., Ourselin, S., & Cardoso, M. J. (2020). Hierarchical Brain Parcellation with Uncertainty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12443 LNCS, pp. 23–31). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60365-6_3

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free