A fully automated method for quantifying and localizing white matter hyperintensities on MR images

Wu, Minjie; Rosano, Caterina; Butters, Meryl; Whyte, Ellen; Nable, Megan; Crooks, Ryan; Meltzer, Carolyn C.; Reynolds, Charles F.; Aizenstein, Howard J.

doi:10.1016/j.pscychresns.2006.09.003

« Previous Next »Psychiatry Research: Neuroimaging
Volume 148, Issue 2 , Pages 133-142, 1 December 2006

A fully automated method for quantifying and localizing white matter hyperintensities on MR images

Minjie Wu
- Department of Electrical and Computer Engineering, University of Pittsburgh, USA
Caterina Rosano
- Department of Epidemiology, University of Pittsburgh, USA
Meryl Butters
- Department of Psychiatry, University of Pittsburgh, USA
Ellen Whyte
- Department of Psychiatry, University of Pittsburgh, USA
Megan Nable
- Department of Psychiatry, University of Pittsburgh, USA
Ryan Crooks
- Department of Epidemiology, University of Pittsburgh, USA
Carolyn C. Meltzer
- Department of Radiology, University of Pittsburgh, USA
Charles F. Reynolds III
- Department of Psychiatry, University of Pittsburgh, USA
Howard J. Aizenstein
- Department of Psychiatry, University of Pittsburgh, USA
- Corresponding author. Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. Tel.: +1 412 624 4997; fax: +1 412 624 0223.

Received 13 December 2005; received in revised form 16 June 2006; accepted 11 September 2006.

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.