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

• Published in: PloS one 2014-08, Vol.9 (8), p.e104011-e104011
• Main Authors: Tsai, Jang-Zern, Peng, Syu-Jyun, Chen, Yu-Wei, Wang, Kuo-Wei, Li, Chen-Hua, Wang, Jing-Yi, Chen, Chi-Jen, Lin, Huey-Juan, Smith, Eric Edward, Wu, Hsiao-Kuang, Sung, Sheng-Feng, Yeh, Poh-Shiow, Hsin, Yue-Loong
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Algorithms; Brain research; Cerebral infarction; Cerebral Infarction - diagnosis; Cerebral Infarction - pathology; Cognitive ability; Computer and Information Sciences; Computer engineering; Computer science; Delineation; Edema; Electrical engineering; Engineering and Technology; Female; Hemispheres; Hemorrhage; Hospitals; Human bias; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Infarction; Ischemia; Magnetic resonance; Magnetic Resonance Imaging; Male; Medicine and Health Sciences; Methods; Middle Aged; Morphology; Multiple sclerosis; Neurology; NMR; Nuclear magnetic resonance; Patients; Reproducibility of Results; Segmentation; Stroke; Substantia alba; Subtraction; White Matter - pathology; Workflow
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0104011

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 83.142±11.742, 84.154±16.086 and 99.988±0.029% 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.