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

Authors

  • Vamsi Ithapu,

    Corresponding author
    1. Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin
    2. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    • Correspondence to: Vamsi Ithapu, 5765 Medical Sciences Center, 1300 University Avenue, Madison 53706, WI. E-mail: ithapu@wisc.edu,

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  • Vikas Singh,

    1. Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin
    2. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
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  • Christopher Lindner,

    1. Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin
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  • Benjamin P. Austin,

    1. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    2. Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
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  • Chris Hinrichs,

    1. Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin
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  • Cynthia M. Carlsson,

    1. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    2. Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
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  • Barbara B. Bendlin,

    1. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    2. Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
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  • Sterling C. Johnson

    1. Wisconsin Alzheimer's Disease Research Center, Madison, Wisconsin
    2. Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
    3. William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin
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Abstract

Precise detection and quantification 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 co-morbid neural injury or cerebral vascular disease burden. WMH in the older population may be small, diffuse, and irregular in shape, and sufficiently heterogeneous within and across subjects. Here, we pose hyperintensity 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 texton filter banks, we provide a suite of effective 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 measure of hyperintensity accumulation, referred to as normalized effective 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. Hum Brain Mapp 35:4219–4235, 2014. © 2014 Wiley Periodicals, Inc.

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