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arXiv · 2506.00498

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

Raghav Mehta·Karthik Gopinath·Ben Glocker·Juan Eugenio Iglesias
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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.

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Raghav Mehta, Karthik Gopinath, Ben Glocker, Juan Eugenio Iglesias. 2025-05-31. UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs. https://arxiv.org/abs/2506.00498

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