  This study investigates a 3D and fully convolutional neural network (CNN) for
subcortical brain structure segmentation in MRI. 3D CNN architectures have been
generally avoided due to their computational and memory requirements during
inference. We address the problem via small kernels, allowing deeper
architectures. We further model both local and global context by embedding
intermediate-layer outputs in the final prediction, which encourages
consistency between features extracted at different scales and embeds
fine-grained information directly in the segmentation process. Our model is
efficiently trained end-to-end on a graphics processing unit (GPU), in a single
stage, exploiting the dense inference capabilities of fully CNNs.
  We performed comprehensive experiments over two publicly available datasets.
First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then,
we report a {\em large-scale} multi-site evaluation over 1112 unregistered
subject datasets acquired from 17 different sites (ABIDE dataset), with ages
ranging from 7 to 64 years, showing that our method is robust to various
acquisition protocols, demographics and clinical factors. Our method yielded
segmentations that are highly consistent with a standard atlas-based approach,
while running in a fraction of the time needed by atlas-based methods and
avoiding registration/normalization steps. This makes it convenient for massive
multi-site neuroanatomical imaging studies. To the best of our knowledge, our
work is the first to study subcortical structure segmentation on such
large-scale and heterogeneous data.
