论文标题
新型的结构尺度不确定性测量和错误保留曲线：应用多发性硬化症
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
论文作者
论文摘要
本文侧重于磁共振成像（MRI）中白质病变（WML）分割的不确定性估计。在一侧，体素尺度的分割误差导致病变的错误描述；另一方面，病变尺度的检测错误导致错误的病变计数。这两个因素在临床上与评估多发性硬化症患者有关。这项工作旨在比较分别捕获与分割和病变检测有关的不同体素和病变规模的不确定性度量的能力。我们的主要贡献是（i）提出不利用素级不确定性的病变规模不确定性的新措施； （ii）扩展误差保留曲线分析框架，以评估病变规模的不确定性度量。我们在58名患者的多中心测试集获得的结果表明，所提出的病变规范的测量在分析措施中取得了最佳性能。所有代码实现均在https://github.com/nataliiamolch/ms_wml_uncs提供
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion-scale uncertainty measures to capture errors related to segmentation and lesion detection, respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measure achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncs
