UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
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: \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.
Raghav Mehta、Karthik Gopinath、Ben Glocker、Juan Eugenio Iglesias
临床医学计算技术、计算机技术
Raghav Mehta,Karthik Gopinath,Ben Glocker,Juan Eugenio Iglesias.UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs[EB/OL].(2025-07-07)[2025-07-16].https://arxiv.org/abs/2506.00498.点此复制
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