Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland.
University Research Priority Program (URPP), Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland.
Hum Brain Mapp. 2022 Apr 1;43(5):1481-1500. doi: 10.1002/hbm.25739. Epub 2021 Dec 7.
White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three algorithms for WMH extraction: FreeSurfer (T1w), UBO Detector (T1w + FLAIR), and FSL's Brain Intensity AbNormality Classification Algorithm (BIANCA; T1w + FLAIR) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, three-dimensional (3D) FLAIR, and two-dimensional (2D) FLAIR images which were used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volumes provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (r = 0.73). BIANCA accomplished the highest DSC (0.602) in 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (r = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: r = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.
脑白质高信号（WMH）是在健康老年人的 MRI 中经常发现的。WMH 也与衰老和认知能力下降有关。在这里，我们比较并验证了三种 WMH 提取算法：FreeSurfer（T1w）、UBO 探测器（T1w+FLAIR）和 FSL 的脑强度异常分类算法（BIANCA；T1w+FLAIR），使用包括认知健康老年人 MRI 数据的纵向数据集（基线 N=231，年龄范围 64-87 岁）。作为参考，我们手动分割了 T1w、三维（3D）FLAIR 和二维（2D）FLAIR 图像中的 WMH，用于评估不同自动算法的分割准确性。此外，我们评估了算法提供的 WMH 体积与 Fazekas 评分和年龄的关系。FreeSurfer 低估了 WMH 体积，其 Dice 相似系数（DSC=0.434）得分最差，但它的 WMH 体积与 Fazekas 评分高度相关（r=0.73）。BIANCA 在 3D FLAIR 图像中实现了最高的 DSC（0.602）。然而，与 Fazekas 评分的关系只是中等，尤其是在 2D FLAIR 图像中（r=0.41），并且在探索个体内轨迹时检测到许多异常 WMH 体积（2D FLAIR：~30%）。UBO 探测器在两种模态下的 DSC 表现与 BIANCA 相似，在不需要定制训练数据集的情况下，在 2D FLAIR 中达到最佳 DSC（0.531）。此外，它与 Fazekas 评分高度相关（2D FLAIR：r=0.80）。总之，我们的结果强调了仔细考虑 WMH 分割算法和 MR 模态选择的重要性。
