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The implication of the thalamus in multiple neurological pathologies makes ita structure of interest for volumetric analysis. In the present work, we havedesigned and implemented a multimodal volumetric deep neural network for thesegmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Currenttools either operate at standard resolution (1 mm3) or use monomodal data. Toachieve the proposed objective, first, a database of semiautomaticallysegmented thalamic nuclei was created using ultra-high resolution T1, T2 andWhite Matter nulled (WMn) images. Then, a novel Deep learning based strategywas designed to obtain the automatic segmentations and trained to improve itsrobustness and accuaracy using a semisupervised approach. The proposed methodwas compared with a related state-of-the-art method showing competitive resultsboth in terms of segmentation quality and efficiency. To make the proposedmethod fully available to the scientific community, a full pipeline able towork with monomodal standard resolution T1 images is also proposed.

DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI