  Deep cerebellar nuclei are a key structure of the cerebellum that are
involved in processing motor and sensory information. It is thus a crucial step
to accurately segment deep cerebellar nuclei for the understanding of the
cerebellum system and its utility in deep brain stimulation treatment. However,
it is challenging to clearly visualize such small nuclei under standard
clinical magnetic resonance imaging (MRI) protocols and therefore precise
segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and
great potential of deep neural networks facilitate automatic patient-specific
segmentation. In this paper, we propose a novel deep learning framework
(referred to as DCN-Net) for fast, accurate, and robust patient-specific
segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion
MRI. DCN-Net effectively encodes contextual information on the patch images
without consecutive pooling operations and adding complexity via proposed
dilated dense blocks. During the end-to-end training, label probabilities of
dentate and interposed nuclei are independently learned with a hybrid loss,
handling highly imbalanced data. Finally, we utilize self-training strategies
to cope with the problem of limited labeled data. To this end, auxiliary
dentate and interposed nuclei labels are created on unlabeled data by using
DCN-Net trained on manual labels. We validate the proposed framework using 7T
B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net
provides better segmentation than atlas-based deep cerebellar nuclei
segmentation tools and other state-of-the-art deep neural networks in terms of
accuracy and consistency. We further prove the effectiveness of the proposed
components within DCN-Net in dentate and interposed nuclei segmentation.
