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.
View on arXiv@article{mehta2025_2506.00498,
title={ UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs },
author={ Raghav Mehta and Karthik Gopinath and Ben Glocker and Juan Eugenio Iglesias },
journal={arXiv preprint arXiv:2506.00498},
year={ 2025 }
}