  The relationship between cognition and white matter hyperintensities (WMH)
volumes often depends on the accuracy of the lesion segmentation algorithm
used. As such, accurate detection and quantification of WMH is of great
interest. Here, we use a deep learning-based WMH segmentation algorithm,
StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used
a subset of subjects (n=20) and obtained manual WMH segmentations by an
experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On
a larger cohort of subjects (n=290), we observed that larger WMH volumes
correlated with worse performance on executive function (P=.004), memory
(P=.01), and language (P=.005).
