  Automated segmentation of white matter hyperintensities (WMHs) is an
essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI).
Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is
particularly useful to visualize and quantify WMHs, a hallmark of cerebral
small vessel disease and Alzheimer's disease (AD). Clinical MRI protocols
migrate to a three-dimensional (3D) FLAIR-weighted acquisition to enable high
spatial resolution in all three voxel dimensions. The current study details the
deployment of deep learning tools to enable automated WMH segmentation and
characterization from 3D FLAIR-weighted images acquired as part of a national
AD imaging initiative.
  Among 441 participants (194 male, mean age: (64.91 +/- 9.32) years) from the
DDI study, two in-house networks were trained and validated across five
national collection sites. Three models were tested on a held-out subset of the
internal data from the 441 participants and an external dataset with 29 cases
from an international collaborator. These test sets were evaluated
independently. Five established WMH performance metrics were used for
comparison against ground truth human-in-the-loop segmentation.
  Results of the three networks tested, the 3D nnU-Net had the best performance
with an average dice similarity coefficient score of 0.76 +/- 0.16, performing
better than both the in-house developed 2.5D model and the SOTA Deep Bayesian
network.
  With the increasing use of 3D FLAIR-weighted images in MRI protocols, our
results suggest that WMH segmentation models can be trained on 3D data and
yield WMH segmentation performance that is comparable to or better than
state-of-the-art without the need for including T1-weighted image series.
