  Whole brain segmentation is an important neuroimaging task that segments the
whole brain volume into anatomically labeled regions-of-interest. Convolutional
neural networks have demonstrated good performance in this task. Existing
solutions, usually segment the brain image by classifying the voxels, or
labeling the slices or the sub-volumes separately. Their representation
learning is based on parts of the whole volume whereas their labeling result is
produced by aggregation of partial segmentation. Learning and inference with
incomplete information could lead to sub-optimal final segmentation result. To
address these issues, we propose to adopt a full volume framework, which feeds
the full volume brain image into the segmentation network and directly outputs
the segmentation result for the whole brain volume. The framework makes use of
complete information in each volume and can be implemented easily. An effective
instance in this framework is given subsequently. We adopt the $3$D
high-resolution network (HRNet) for learning spatially fine-grained
representations and the mixed precision training scheme for memory-efficient
training. Extensive experiment results on a publicly available $3$D MRI brain
dataset show that our proposed model advances the state-of-the-art methods in
terms of segmentation performance. Source code is publicly available at
https://github.com/microsoft/VoxHRNet.
