UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
Journal:
arXiv
Published Date:
May 31, 2025
Abstract
We propose UNSURF, a novel uncertainty measure for cortical surface
reconstruction of clinical brain MRI scans of any orientation, resolution, and
contrast. It relies on the discrepancy between predicted voxel-wise signed
distance functions (SDFs) and the actual SDFs of the fitted surfaces. Our
experiments on real clinical scans show that traditional uncertainty measures,
such as voxel-wise Monte Carlo variance, are not suitable for modeling the
uncertainty of surface placement. Our results demonstrate that UNSURF estimates
correlate well with the ground truth errors and: \textit{(i)}~enable effective
automated quality control of surface reconstructions at the subject-, parcel-,
mesh node-level; and \textit{(ii)}~improve performance on a downstream
Alzheimer's disease classification task.
