In this study, we proposed the first deep learning and 7T MR imaging based dentate and interposed nuclei segmentation framework. We introduce dilated dense blocks to effectively encode contextual information on different receptive fields in an encoder-decoder network. Training of the proposed network is optimized with a multi-class hybrid segmentation loss, handling a class imbalance problem. Moreover, a self-training strategy facilitates the training of the proposed network by exploiting auxiliary labels. The proposed framework significantly outperforms an atlas-based deep cerebellar nuclei segmentation tool and state-of-the-art deep neural networks in terms of accuracy and consistency.
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