  In this paper, we propose generating synthetic multiple sclerosis (MS)
lesions on MRI images with the final aim to improve the performance of
supervised machine learning algorithms, therefore avoiding the problem of the
lack of available ground truth. We propose a two-input two-output fully
convolutional neural network model for MS lesion synthesis in MRI images. The
lesion information is encoded as discrete binary intensity level masks passed
to the model and stacked with the input images. The model is trained end-to-end
without the need for manually annotating the lesions in the training set. We
then perform the generation of synthetic lesions on healthy images via
registration of patient images, which are subsequently used for data
augmentation to increase the performance for supervised MS lesion detection
algorithms. Our pipeline is evaluated on MS patient data from an in-house
clinical dataset and the public ISBI2015 challenge dataset. The evaluation is
based on measuring the similarities between the real and the synthetic images
as well as in terms of lesion detection performance by segmenting both the
original and synthetic images individually using a state-of-the-art
segmentation framework. We also demonstrate the usage of synthetic MS lesions
generated on healthy images as data augmentation. We analyze a scenario of
limited training data (one-image training) to demonstrate the effect of the
data augmentation on both datasets. Our results significantly show the
effectiveness of the usage of synthetic MS lesion images. For the ISBI2015
challenge, our one-image model trained using only a single image plus the
synthetic data augmentation strategy showed a performance similar to that of
other CNN methods that were fully trained using the entire training set,
yielding a comparable human expert rater performance
