Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction

PURPOSE It has been reported that increased white matter lesions (WML) is one of the risk factors for stroke. To quantify WML objectively with the presence of acute infarcts, we proposed an automated segmentation scheme to locate WMLs in combined T1-weighted MRI, fluid attenuation inversion recovery (FLAIR) and diffusion weighted imaging (DWI). MATERIALS AND METHODS The proposed method detects WMLs by a coarse-to-fine mathematical morphology method. It has been evaluated quantitatively and qualitatively using voxel-based, volume-based, score-based, and atlas-based approaches on MRI data of 91 subjects with acute infarction. RESULT The proposed WML detection algorithm yields average sensitivity, positive predictive value and similarity index of 0.803, 0.818, and 0.836, respectively. Experimental results demonstrated that the segmentation from the proposed method is in high agreement with that from manual segmentation (intraclass correlation coefficient=0.9892), and with a good correlation with visual scores (R=0.8442, p<0.0001).

[1]  Anil F. Ramlackhansingh,et al.  Lesion identification using unified segmentation-normalisation models and fuzzy clustering , 2008, NeuroImage.

[2]  Marko Wilke,et al.  Manual, semi-automated, and automated delineation of chronic brain lesions: A comparison of methods , 2011, NeuroImage.

[3]  A. Hofman,et al.  Silent Brain Infarcts and White Matter Lesions Increase Stroke Risk in the General Population: The Rotterdam Scan Study , 2003, Stroke.

[4]  Stefan Klöppel,et al.  A comparison of different automated methods for the detection of white matter lesions in MRI data , 2011, NeuroImage.

[5]  Klaus P. Ebmeier,et al.  White matter hyperintensities in late life depression: a systematic review , 2007, Journal of Neurology, Neurosurgery, and Psychiatry.

[6]  K. McGraw,et al.  Forming inferences about some intraclass correlation coefficients. , 1996 .

[7]  P. Scheltens,et al.  A New Rating Scale for Age-Related White Matter Changes Applicable to MRI and CT , 2001, Stroke.

[8]  Lars Kai Hansen,et al.  Segmentation of age-related white matter changes in a clinical multi-center study , 2008, NeuroImage.

[9]  Pélagie M. Beeson,et al.  Cost function masking during normalization of brains with focal lesions: Still a necessity? , 2010, NeuroImage.

[10]  Koen L. Vincken,et al.  Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.

[11]  Wei Wen,et al.  The topography of white matter hyperintensities on brain MRI in healthy 60- to 64-year-old individuals , 2004, NeuroImage.

[12]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.

[13]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[14]  E. Mori,et al.  Impact of White Matter Changes on Clinical Manifestation of Alzheimer’s Disease: A Quantitative Study , 2000, Stroke.

[15]  D. Briley,et al.  Does leukoaraiosis predict morbidity and mortality? , 2000, Neurology.

[16]  L. Harrell,et al.  The relationship of high-intensity signals on magnetic resonance images to cognitive and psychiatric state in Alzheimer's disease. , 1991, Archives of neurology.

[17]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[18]  N. Tomura,et al.  Comparison of Multishot Echo-Planar Fluid-Attenuated Inversion-Recovery Imaging with Fast Spin-Echo Fluid-Attenuated Inversion-Recovery and T2-Weighted Imaging in Depiction of White Matter Lesions , 2002, Journal of computer assisted tomography.

[19]  K. Wong,et al.  Extent of white matter lesions is related to acute subcortical infarcts and predicts further stroke risk in patients with first ever ischaemic stroke , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[20]  Karl J. Friston,et al.  Spatial normalization of lesioned brains: Performance evaluation and impact on fMRI analyses , 2007, NeuroImage.

[21]  C. Reynolds,et al.  A fully automated method for quantifying and localizing white matter hyperintensities on MR images , 2006, Psychiatry Research: Neuroimaging.

[22]  Salvador Ruiz-Correa,et al.  Morphology-based hypothesis testing in discrete random fields: A non-parametric method to address the multiple-comparison problem in neuroimaging , 2011, NeuroImage.

[23]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[24]  Josep Marco-Pallarés,et al.  Analysis of automated methods for spatial normalization of lesioned brains , 2012, NeuroImage.

[25]  Joost Janssen,et al.  Cerebral volume measurements and subcortical white matter lesions and short‐term treatment response in late life depression , 2007, International journal of geriatric psychiatry.

[26]  R Kikinis,et al.  Older people with impaired mobility have specific loci of periventricular abnormality on MRI , 2002, Neurology.

[27]  Wiro J. Niessen,et al.  White matter lesion extension to automatic brain tissue segmentation on MRI , 2009, NeuroImage.

[28]  C. Nemeroff,et al.  Occult subcortical magnetic resonance findings in elderly depressives , 1991 .

[29]  S. Black,et al.  National Institute of Neurological Disorders and Stroke–Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards , 2006, Stroke.

[30]  Frederik Barkhof,et al.  Imaging of White Matter Lesions , 2002, Cerebrovascular Diseases.

[31]  H. Gräfin von Einsiedel,et al.  Detection of Acute Brainstem Infarction by Using DWI/MRI , 2004, European Neurology.

[32]  Frithjof Kruggel,et al.  Texture-based segmentation of diffuse lesions of the brain’s white matter , 2008, NeuroImage.

[33]  P. Scheltens,et al.  Impact of White Matter Hyperintensities Scoring Method on Correlations With Clinical Data: The LADIS Study , 2006, Stroke.

[34]  Belma Dogdas,et al.  Segmentation of skull and scalp in 3‐D human MRI using mathematical morphology , 2005, Human brain mapping.

[35]  C. Jack,et al.  FLAIR histogram segmentation for measurement of leukoaraiosis volume , 2001, Journal of magnetic resonance imaging : JMRI.

[36]  K. Krishnan,et al.  Neuroanatomical substrates of depression in the elderly , 2005, European Archives of Psychiatry and Clinical Neuroscience.

[37]  Frithjof Kruggel,et al.  White matter lesion segmentation based on feature joint occurrence probability and chi2 random field theory from magnetic resonance (MR) images , 2010, Pattern Recognit. Lett..

[38]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[39]  Christos Davatzikos,et al.  Computer-assisted Segmentation of White Matter Lesions in 3d Mr Images Using Support Vector Machine 1 , 2022 .

[40]  K. Krishnan,et al.  Subcortical hyperintensities on brain magnetic resonance imaging: A comparison between late age onset and early onset elderly depressed subjects , 1991, Neurobiology of Aging.

[41]  Johan H. C. Reiber,et al.  Fully automatic segmentation of white matter hyperintensities in MR images of the elderly , 2005, NeuroImage.

[42]  Daniel Rueckert,et al.  Generalised Overlap Measures for Assessment of Pairwise and Groupwise Image Registration and Segmentation , 2005, MICCAI.