提出新方法精准量化脑皮层重建误差，助力临床MRI质量控制与疾病诊断。
UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIs
- 基于预测与真实表面距离函数的差异计算不确定性
- 误差估计与真实误差高度相关，可识别低质量重建区域
- 适用于任意方向分辨率的临床MRI，适合医生和研究者使用
我们提出UNSURF，一种针对任意方向、分辨率和对比度的临床脑MRI进行皮层表面重建的新型不确定性度量。该方法基于预测体素级有符号距离函数（SDF）与拟合表面实际SDF之间的差异。在真实临床扫描上的实验表明，传统不确定性度量如体素级蒙特卡洛方差不适用于表面定位不确定性建模。结果表明，UNSURF估计与真实误差高度相关，能有效实现受试者、区域、网格节点级别的自动化质量控制，并提升阿尔茨海默病分类任务的性能。
原文摘要 · Abstract (English)
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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