  Segmentation and quantification of white matter hyperintensities (WMHs) are
of great importance in studying and understanding various neurological and
geriatric disorders. Although automatic methods have been proposed for WMH
segmentation on magnetic resonance imaging (MRI), manual corrections are often
necessary to achieve clinically practical results. Major challenges for WMH
segmentation stem from their inhomogeneous MRI intensities, random location and
size distributions, and MRI noise. The presence of other brain anatomies or
diseases with enhanced intensities adds further difficulties. To cope with
these challenges, we present a specifically designed fully convolutional neural
network (FCN) with residual connections to segment WMHs by using combined T1
and fluid-attenuated inversion recovery (FLAIR) images. Our customized FCN is
designed to be straightforward and generalizable, providing efficient
end-to-end training due to its enhanced information propagation. We tested our
method on the open WMH Segmentation Challenge MICCAI2017 dataset, and, despite
our method's relative simplicity, results show that it performs amongst the
leading techniques across five metrics. More importantly, our method achieves
the best score for hausdorff distance and average volume difference in testing
datasets from two MRI scanners that were not included in training,
demonstrating better generalization ability of our proposed method over its
competitors.
