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

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks