Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer’s disease risk and aging studies

Vamsi Ithapu, Vikas Singh, Christopher Lindner, Benjamin P. Austin, Chris Hinrichs, Cynthia M. Carlsson, Barbara B. Bendlin and Sterling C. Johnson

Human Brain Mapping. 2014 Aug;35(8):4219-35. Epub 2014 Feb 7.


Precise detection and quanti cation of white matter hyperintensities (WMH) observed in T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) is of substantial interest in aging, and age related neurological disorders such as Alzheimer's disease (AD). This is mainly because WMH may reflect comorbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, di use and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyper intensity detection as a supervised inference problem and adapt two learning models, specifically, Support Vector Machines and Random Forests, for this task. Using texture features engineered by text on lter banks, we provide a suite of e ective segmentation methods for this problem. Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions. A new measure of hyperintensity accumulation, referred to as normalized E ective WMH Volume, is shown to be associated with dementia in older adults and parental family history in cognitively normal subjects. We provide an open source library for hyperintensity detection and accumulation (interfaced with existing neuroimaging tools), that can be adapted for segmentation problems in other neuroimaging studies.


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