  Segmenting deep brain structures from magnetic resonance images is important
for patient diagnosis, surgical planning, and research. Most current
state-of-the-art solutions follow a segmentation-by-registration approach,
where subject MRIs are mapped to a template with well-defined segmentations.
However, registration-based pipelines are time-consuming, thus, limiting their
clinical use. This paper uses deep learning to provide a robust and efficient
deep brain segmentation solution. The method consists of a pre-processing step
to conform all MRI images to the same orientation, followed by a convolutional
neural network using the nnU-Net framework. We use a total of 14 datasets from
both research and clinical collections. Of these, seven were used for training
and validation and seven were retained for independent testing. We trained the
network to segment 30 deep brain structures, as well as a brain mask, using
labels generated from a registration-based approach. We evaluated the
generalizability of the network by performing a leave-one-dataset-out
cross-validation, and extensive testing on external datasets. Furthermore, we
assessed cross-domain transportability by evaluating the results separately on
different domains. We achieved an average DSC of 0.89 $\pm$ 0.04 on the
independent testing datasets when compared to the registration-based gold
standard. On our test system, the computation time decreased from 42 minutes
for a reference registration-based pipeline to 1 minute. Our proposed method is
fast, robust, and generalizes with high reliability. It can be extended to the
segmentation of other brain structures. The method is publicly available on
GitHub, as well as a pip package for convenient usage.
