Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2012.02.034
Title: Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts
Authors: Wang, Y.
Catindig, J.A.
Hilal, S.
Soon, H.W.
Ting, E.
Wong, T.Y. 
Venketasubramanian, N.
Chen, C.
Qiu, A. 
Keywords: Cortical infarct
Lacunar infarct
Magnetic resonance imaging
White matter hyperintensity
Issue Date: 1-May-2012
Source: Wang, Y.,Catindig, J.A.,Hilal, S.,Soon, H.W.,Ting, E.,Wong, T.Y.,Venketasubramanian, N.,Chen, C.,Qiu, A. (2012-05-01). Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts. NeuroImage 60 (4) : 2379-2388. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2012.02.034
Abstract: 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. © 2012 Elsevier Inc..
Source Title: NeuroImage
URI: http://scholarbank.nus.edu.sg/handle/10635/52540
ISSN: 10538119
DOI: 10.1016/j.neuroimage.2012.02.034
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