UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916828580675584 |
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| author | Mehta, Raghav Gopinath, Karthik Glocker, Ben Iglesias, Juan Eugenio |
| author_facet | Mehta, Raghav Gopinath, Karthik Glocker, Ben Iglesias, Juan Eugenio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs Mehta, Raghav Gopinath, Karthik Glocker, Ben Iglesias, Juan Eugenio Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00498 |