  Segmentation is one of the most important tasks in MRI medical image analysis
and is often the first and the most critical step in many clinical
applications. In brain MRI analysis, head segmentation is commonly used for
measuring and visualizing the brain's anatomical structures and is also a
necessary step for other applications such as current-source reconstruction in
electroencephalography and magnetoencephalography (EEG/MEG). Here we propose a
deep learning framework that can segment brain, skull, and extra-cranial tissue
using only T1-weighted MRI as input. In addition, we describe a robust method
for training the model in the presence of noisy labels.
