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

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: $\mathit{\text{(i)}}$~enable effective automated quality control of surface reconstructions at the subject-, parcel-, mesh node-level; and $\mathit{\text{(ii)}}$~improve performance on a downstream Alzheimer’s disease classification task.