UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs

TL;DR

UNSURF introduces a new uncertainty measure for cortical surface reconstruction in clinical brain MRIs, effectively identifying errors and enhancing downstream disease classification.

Contribution

The paper presents UNSURF, a novel uncertainty quantification method that outperforms traditional measures for cortical surface reconstruction in diverse MRI scans.

Findings

UNSURF correlates well with ground truth errors

Enables automated quality control of surface reconstructions

Improves Alzheimer's disease classification performance

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.