mlsanityData to 14 Oct 2026

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

arXiv 2506.00498 ↗First posted 31 May 2025eess.IV cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 31 May 2025.

Builds on

23 of 39 references.

References · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  4. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  5. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 237
  6. 2020Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learningcited by 210
  7. 2019Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfacescited by 186
  8. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  9. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  10. 2019PHiSeg: Capturing Uncertainty in Medical Image Segmentationcited by 128
  11. 2022Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Taskscited by 49
  12. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
11 more
  1. 2021QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Resultscited by 28
  2. 2020DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstructioncited by 27
  3. 2022Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networkscited by 27
  4. 2022CortexODE: Learning Cortical Surface Reconstruction by Neural ODEscited by 22
  5. 2021FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRIcited by 19
  6. 2023Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentationcited by 7
  7. 2024Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRIcited by 7
  8. 2023Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studiescited by 6
  9. 2020Sampling possible reconstructions of undersampled acquisitions in MR imagingcited by 5
  10. 2023Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Modelscited by 5
  11. 2024Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimagingcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Uncertainty-Aware Cortical Surface Reconstruction and Spherical Registration for Heterogeneous Brain MRIcited by 0