  Automatic methods for measuring normalized regional brain volumes from MRI
data are a key tool to help in the objective diagnostic and follow-up of many
neurological diseases. To estimate such regional brain volumes, the
intracranial cavity volume is commonly used for normalization. In this paper,
we present an accurate and efficient approach to automatically segment the
intracranial cavity using a volumetric 3D convolutional neural network and a
new 3D patch extraction strategy specially adapted to deal with the traditional
low number of training cases available in supervised segmentation and the
memory limitations of modern GPUs. The proposed method is compared with recent
state-of-the-art methods and the results show an excellent accuracy and
improved performance in terms of computational burden.
