We here summarise and discuss our published work on semantic segmentation of 3D neonatal brain MRI with deep networks. In addition to developing an accurate, end-to-end segmentation pipeline specifically designed for neonatal brain MRI, we investigated two approaches that can help alleviate the problem of label scarcity often faced in neonatal imaging. First, we examined different strategies of distributing a limited budget of annotated 2D slices over 3D whole-brain images. In the second approach, we compared the segmentation performance of pre-trained models with different strategies of fine-tuning on a small subset of preterm infants. We illustrated our findings using publicly available MRI scans obtained retrospectively from the Developing Human Connectome Project (ages at scan: 26-45 weeks).