# Hierarchical brain parcellation with uncertainty

arXiv: https://arxiv.org/abs/2009.07573 · HTML: https://mlsanity.com/paper/2009.07573/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [Acquisition-invariant brain MRI segmentation with informative uncertainties](https://mlsanity.com/paper/2111.04094/) (2021)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)

## Abstract (arXiv)

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.