Department of Pharmacology, National University of Singapore, Singapore.
Neuroimage. 2012 May 1;60(4):2379-88. doi: 10.1016/j.neuroimage.2012.02.034. Epub 2012 Feb 22.
Cerebral abnormalities such as white matter hyperintensity (WMH), cortical infarct (CI), and lacunar infarct (LI) are of clinical importance and frequently present in patients with stroke and dementia. Up to date, there are limited algorithms available to automatically delineate these cerebral abnormalities partially due to their complex appearance in MR images. In this paper, we describe an automated multi-stage segmentation approach for labeling the WMH, CI, and LI using multi-modal MR images. We first automatically segment brain tissues (white matter, gray matter, and CSF) based on the T1-weighted image and then identify hyperintense voxels based on the fluid attenuated inversion recovery (FLAIR) image. We finally label the WMH, CI, and LI based on the T1-weighted, T2-weighted, and FLAIR images. The segmentation accuracy is evaluated using a community-based sample of 272 old adults. Our results show that the automated segmentation of the WMH, CI, and LI is comparable with manual labeling in terms of spatial location, volume, and the number of lacunes. Additionally, the WMH volume is highly correlated with the visual grading score based on the Age-Related White Matter Changes (ARWMC) protocol. The evaluations against the manual labeling and ARWMC visual grading suggest that our algorithm provides reasonable segmentation accuracy for the WMH, CI, and LI.
脑异常，如脑白质高信号（WMH）、皮质梗死（CI）和腔隙性梗死（LI）具有临床重要性，并且常存在于中风和痴呆患者中。迄今为止，由于其在磁共振图像中复杂的表现，可用的自动勾画这些脑异常的算法有限。在本文中，我们描述了一种使用多模态磁共振图像自动标记 WMH、CI 和 LI 的多阶段分割方法。我们首先基于 T1 加权图像自动分割脑区（白质、灰质和脑脊液），然后基于液体衰减反转恢复（FLAIR）图像识别高信号体素。最后，我们基于 T1 加权、T2 加权和 FLAIR 图像标记 WMH、CI 和 LI。使用社区的 272 名老年人样本评估分割准确性。我们的结果表明，在空间位置、体积和腔隙数量方面，WMH、CI 和 LI 的自动分割与手动标记具有可比性。此外，WMH 体积与基于年龄相关性脑白质改变（ARWMC）协议的视觉分级评分高度相关。与手动标记和 ARWMC 视觉分级的评估表明，我们的算法为 WMH、CI 和 LI 提供了合理的分割准确性。
