论文标题
分层大脑划分和不确定性
Hierarchical brain parcellation with uncertainty
论文作者
论文摘要
用于脑部分层的许多地图酶是分层组织的，将大脑逐渐分为较小的子区域。但是，最新的拟释放方法倾向于忽略这种结构，并将标签视为“平坦”。我们介绍了一种层次感知的脑部分割方法，该方法通过预测标签树中每个分支的决策来起作用。我们进一步展示了该方法如何用于为此标签树中每个分支分开建模不确定性。我们的方法超出了平坦的不确定性方法的性能，同时还提供了分解的不确定性估计，使我们能够在标签层次结构的任何级别上获得自一致的分割和不确定性图。我们证明了这些决策特异性不确定性图的简单方式可用于在标签树的任何级别提供不确定性阈值的组织图。
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.
