Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

中文翻译：

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TAPAS：多发性硬化症概率图自动分割的阈值方法。


总脑白质病变 (WML) 体积是多发性硬化症 (MS) 研究中最广泛建立的磁共振成像 (MRI) 结果测量指标。为了估计 WML 体积，有许多自动分割方法可用，但手动划分仍然是黄金标准方法。自动方法通常会生成概率图，应用阈值来创建病变分割掩模。不幸的是，很少有方法系统地确定所采用的阈值。许多方法使用手动选择的阈值，从而将人为错误和偏差引入自动化程序中。在本研究中，我们提出并验证了一种自动阈值算法，即多发性硬化症概率图自动分割阈值方法 (TAPAS)，以获得 T2 加权 (T2) 高信号 WML 概率图自动分割的特定于受试者的阈值估计。该方法使用多模态 MRI，应用自动分割算法来获得概率图。我们获得了最大化 Sørensen-Dice 相似系数 (DSC) 的真实主题特定阈值。然后，使用广义加性模型根据体积的朴素估计对特定于受试者的阈值进行建模。应用该模型，我们预测未用于训练的数据中的特定于主题的阈值。我们使用两个数据集运行蒙特卡罗重采样分割样本交叉验证（100 个验证集）：第一个数据集是在 Philips 3 Tesla (3T) 扫描仪 (n = 94) 上从约翰霍普金斯医院 (JHH) 获得的，另一个是在第二个数据是在布莱根妇女医院 (BWH) 使用西门子 3T 扫描仪收集的 (n = 40)。通过所提出的自动化技术，在 JHH 数据中，我们发现受试者级绝对误差平均减少了 0。手动体积每增加 1 mL，增加 1 mL。使用 Bland-Altman 分析，我们发现应用 TAPAS 时，与组级阈值相关的体积偏差得到了缓解。 BWH 数据显示使用组级阈值或 TAPAS 进行的类似绝对误差估计，可能是因为 Bland-Altman 分析表明与组或 TAPAS 体积估计值不存在相关的系统偏差。目前的研究提出了第一个经过验证的全自动方法，用于特定受试者阈值预测以分割脑损伤。