This paper explores uncertainty quantification (UQ) as an indicator of the
trustworthiness of automated deep-learning (DL) tools in the context of white
matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of
multiple sclerosis (MS) patients. Our study focuses on two principal aspects of
uncertainty in structured output segmentation tasks. Firstly, we postulate that
a good uncertainty measure should indicate predictions likely to be incorrect
with high uncertainty values. Second, we investigate the merit of quantifying
uncertainty at different anatomical scales (voxel, lesion, or patient). We
hypothesize that uncertainty at each scale is related to specific types of
errors. Our study aims to confirm this relationship by conducting separate
analyses for in-domain and out-of-domain settings. Our primary methodological
contributions are (i) the development of novel measures for quantifying
uncertainty at lesion and patient scales, derived from structural prediction
discrepancies, and (ii) the extension of an error retention curve analysis
framework to facilitate the evaluation of UQ performance at both lesion and
patient scales. The results from a multi-centric MRI dataset of 172 patients
demonstrate that our proposed measures more effectively capture model errors at
the lesion and patient scales compared to measures that average voxel-scale
uncertainty values. We provide the UQ protocols code at
this https URL

该论文研究了自动化深度学习工具在多发性硬化症患者磁共振成像扫描中的白质病变分割中的不确定性量化作为其可靠性指标。研究探讨了不确定性在结构化输出分割任务中的两个主要方面，并提出了新的衡量不确定性的方法。通过分析来自多中心的 MRI 数据集，结果表明我们的方法能够更有效地捕捉模型在病变和患者尺度上的错误。