Brain tissue segmentation is a prerequisite for many subsequent automatic quantitative analysis techniques. As with many medical imaging tasks, a shortage of manually annotated training data is a limiting factor which is not easily overcome, particularly using recent deep-learning technology. We present a deep convolutional neural network (CNN) trained on just 2 publicly available manually annotated volumes, trained to annotate 8 tissue types in neonatal T2 MRI. The network makes use of several recent deep-learning techniques as well as artificial augmentation of the training data, to achieve state-of-the- art results on public challenge data.