Automated segmentation of white matter hyperintensities (WMHs) is an essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer's disease (AD). Clinical MRI protocols migrate to a three-dimensional (3D) FLAIR-weighted acquisition to enable high spatial resolution in all three voxel dimensions. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Among 441 participants (194 male, mean age: (64.91 +/- 9.32) years) from the DDI study, two in-house networks were trained and validated across five national collection sites. Three models were tested on a held-out subset of the internal data from the 441 participants and an external dataset with 29 cases from an international collaborator. These test sets were evaluated independently. Five established WMH performance metrics were used for comparison against ground truth human-in-the-loop segmentation. Results of the three networks tested, the 3D nnU-Net had the best performance with an average dice similarity coefficient score of 0.76 +/- 0.16, performing better than both the in-house developed 2.5D model and the SOTA Deep Bayesian network. With the increasing use of 3D FLAIR-weighted images in MRI protocols, our results suggest that WMH segmentation models can be trained on 3D data and yield WMH segmentation performance that is comparable to or better than state-of-the-art without the need for including T1-weighted image series.

翻译：白质高信号（WMH）的自动分割是磁共振成像（MRI）神经影像分析中的关键步骤。液体衰减反转恢复（FLAIR加权）是一种特别适用于可视化并量化WMH的MRI对比度，WMH是脑小血管疾病和阿尔茨海默病（AD）的标志性特征。临床MRI方案正转向三维（3D）FLAIR加权采集，以实现所有三个体素维度的高空间分辨率。本研究详述了利用深度学习工具，从全国性AD影像计划中获取的3D FLAIR加权图像中实现WMH自动分割与表征的过程。基于DDI研究中441名参与者（男性194名，平均年龄：(64.91 ± 9.32)岁），在两个内部自建网络上进行训练，并在五个国家级采集站点进行验证。三种模型在来自441名参与者内部数据的保留子集和来自国际合作者的29例外部数据集上进行测试。这些测试集被独立评估。采用五项已建立的WMH性能指标，与人工参与的黄金标准分割结果进行对比。结果显示，在测试的三种网络中，3D nnU-Net表现最佳，平均Dice相似系数得分为0.76 ± 0.16，优于内部开发的2.5D模型和SOTA深度贝叶斯网络。随着MRI方案中3D FLAIR加权图像的广泛应用，我们的结果表明，基于3D数据训练的WMH分割模型可以达到与当前最优方法相当甚至更优的性能，且无需包含T1加权图像序列。