title: A fully automated method for quantifying and localizing white matter hyperintensities on MR images.
abstract: White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.
authors.affiliation: Department of Electrical and Computer Engineering, University of Pittsburgh, USA.
authors.forename: Minjie
authors.initials: M
authors.lastname: Wu
authors.forename: Caterina
authors.initials: C
authors.lastname: Rosano
authors.forename: Meryl
authors.initials: M
authors.lastname: Butters
authors.forename: Ellen
authors.initials: E
authors.lastname: Whyte
authors.forename: Megan
authors.initials: M
authors.lastname: Nable
authors.forename: Ryan
authors.initials: R
authors.lastname: Crooks
authors.forename: Carolyn C
authors.initials: CC
authors.lastname: Meltzer
authors.forename: Charles F
authors.initials: CF
authors.lastname: Reynolds
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Aizenstein
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001008:Anxiety Disorders / Q000175:diagnosis; D001284:Atrophy; D001921:Brain / Q000473:pathology*; D015897:Comorbidity; D003865:Major Depressive Disorder / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D004292:Dominance, Cerebral / Q000502:physiology; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology*