  Medical imaging is a domain which suffers from a paucity of manually
annotated data for the training of learning algorithms. Manually delineating
pathological regions at a pixel level is a time consuming process, especially
in 3D images, and often requires the time of a trained expert. As a result,
supervised machine learning solutions must make do with small amounts of
labelled data, despite there often being additional unlabelled data available.
Whilst of less value than labelled images, these unlabelled images can contain
potentially useful information. In this paper we propose combining both
labelled and unlabelled data within a GAN framework, before using the resulting
network to produce images for use when training a segmentation network. We
explore the task of deep grey matter multi-class segmentation in an AD dataset
and show that the proposed method leads to a significant improvement in
segmentation results, particularly in cases where the amount of labelled data
is restricted. We show that this improvement is largely driven by a greater
ability to segment the structures known to be the most affected by AD, thereby
demonstrating the benefits of exposing the system to more examples of
pathological anatomical variation. We also show how a shift in domain of the
training data from young and healthy towards older and more pathological
examples leads to better segmentations of the latter cases, and that this leads
to a significant improvement in the ability for the computed segmentations to
stratify cases of AD.
