@article{b4a96d27836942d5adf11aefd32952d1,
title = "Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury",
keywords = "Neuroimaging, brain imaging, magnetic resonance imaging, machine learning, random forest decision tree, deep learning, TBI",
author = "David Tate and Wilde, \{Elisabeth A.\} and Taylor, \{Brian A.\} and Stone, \{James R.\} and Harvey Levin and Bigler, \{Erin D.\} and Scheibel, \{Randall S.\} and Newsome, \{Mary R.\} and Mayer, \{Andrew R.\} and Abildskov, \{Tracy J.\} and Black, \{Garrett M.\} and Lennon, \{Michael J.\} and York, \{Gerald E.\} and Rajan Agarwal and Jorge DeVillasante and Ritter, \{John L.\} and Walker, \{Peter B.\} and Ahlers, \{Stephen T.\} and Tustison, \{Nicholas J.\}",
note = "Background: White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI).",
year = "2016",
month = nov,
day = "11",
doi = "10.1080/02699052.2016.1222080",
language = "American English",
volume = "30",
journal = "Brain Injury",
}