Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction

Jang Zern Tsai, Syu Jyun Peng, Yu Wei Chen, Kuo Wei Wang, Chen Hua Li, Jing Yi Wang, Chi Jen Chen, Huey Juan Lin, Eric Edward Smith, Hsiao Kuang Wu, Sheng Feng Sung, Poh Shiow Yeh, Yue Loong Hsin

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


White matter hyperintensities (WMHs) of presumed vascular origin are common in ageing population, especially in patients with acute cerebral infarction and the volume has been reported to be associated with mental impairment and the risk of hemorrhage from antithrombotic agents. WMHs delineation can be computerized to minimize human bias. However, the presence of cerebral infarcts greatly degrades the accuracy of WMHs detection and thus limits the application of computerized delineation to patients with acute cerebral infarction. We propose a computer-assisted segmentation method to depict WMHs in the presence of cerebral infarcts in combined T1-weighted, fluid attenuation inversion recovery, and diffusion-weighted magnetic resonance imaging (MRI). The proposed method detects WMHs by empirical threshold and atlas information, with subtraction of white matter voxels affected by acute infarction. The method was derived using MRI from 25 hemispheres with WMHs only and 13 hemispheres with both WMHs and cerebral infarcts. Similarity index (SI) and correlation were utilized to assess the agreement between the new automated method and a gold standard visually guided semi-automated method done by an expert rater. The proposed WMHs segmentation approach produced average SI, sensitivity and specificity of 83.1426 11.742, 84.154616.086 and 99.98860.029% with WMHs only and of 68.826±14.036, 74.381±18.473 and 99.956±0.054% with both WMHs and cerebral infarcts in the derivation cohort. The performance of the proposed method with an external validation cohort was also highly consistent with that of the experienced rater.

Original languageEnglish
Article numbere104011
JournalPLoS ONE
Issue number8
StatePublished - 15 Aug 2014


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