The accurate assessment of White matter hyperintensities (WMH) burden is of
crucial importance for epidemiological studies to determine association between
WMHs, cognitive and clinical data. The manual delineation of WMHs is tedious,
costly and time consuming. This is further complicated by the fact that other
pathological features (i.e. stroke lesions) often also appear as hyperintense.
Several automated methods aiming to tackle the challenges of WMH segmentation
have been proposed, however cannot differentiate between WMH and strokes. Other
methods, capable of distinguishing between different pathologies in brain MRI,
are not designed with simultaneous WMH and stroke segmentation in mind. In this
work we propose to use a convolutional neural network (CNN) that is able to
segment hyperintensities and differentiate between WMHs and stroke lesions.
Specifically, we aim to distinguish between WMH pathologies from those caused
by stroke lesions due to either cortical, large or small subcortical infarcts.
As far as we know, this is the first time such differentiation task has
explicitly been proposed. The proposed fully convolutional CNN architecture, is
comprised of an analysis path, that gradually learns low and high level
features, followed by a synthesis path, that gradually combines and up-samples
the low and high level features into a class likelihood semantic segmentation.
Quantitatively, the proposed CNN architecture is shown to outperform other well
established and state-of-the-art algorithms in terms of overlap with manual
expert annotations. Clinically, the extracted WMH volumes were found to
correlate better with the Fazekas visual rating score. Additionally, a
comparison of the associations found between clinical risk-factors and the WMH
volumes generated by the proposed method, were found to be in line with the
associations found with the expert-annotated volumes.