  Translational brain research using Magnetic Resonance Imaging (MRI) is
becoming increasingly popular as animal models are an essential part of
scientific studies and more ultra-high-field scanners are becoming available.
Some disadvantages of MRI are the availability of MRI scanners and the time
required for a full scanning session (it usually takes over 30 minutes).
Privacy laws and the 3Rs ethics rule also make it difficult to create large
datasets for training deep learning models. Generative Adversarial Networks
(GANs) can perform data augmentation with higher quality than other techniques.
In this work, the alpha-GAN architecture is used to test its ability to produce
realistic 3D MRI scans of the rat brain. As far as the authors are aware, this
is the first time that a GAN-based approach has been used for data augmentation
in preclinical data. The generated scans are evaluated using various
qualitative and quantitative metrics. A Turing test conducted by 4 experts has
shown that the generated scans can trick almost any expert. The generated scans
were also used to evaluate their impact on the performance of an existing deep
learning model developed for segmenting the rat brain into white matter, grey
matter and cerebrospinal fluid. The models were compared using the Dice score.
The best results for whole brain and white matter segmentation were obtained
when 174 real scans and 348 synthetic scans were used, with improvements of
0.0172 and 0.0129, respectively. Using 174 real scans and 87 synthetic scans
resulted in improvements of 0.0038 and 0.0764 for grey matter and CSF
segmentation, respectively. Thus, by using the proposed new normalisation layer
and loss functions, it was possible to improve the realism of the generated rat
MRI scans and it was shown that using the generated data improved the
segmentation model more than using the conventional data augmentation.

Image and Video Processing;Computer Vision and Pattern Recognition