  The implication of the thalamus in multiple neurological pathologies makes it
a structure of interest for volumetric analysis. In the present work, we have
designed and implemented a multimodal volumetric deep neural network for the
segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Current
tools either operate at standard resolution (1 mm3) or use monomodal data. To
achieve the proposed objective, first, a database of semiautomatically
segmented thalamic nuclei was created using ultra-high resolution T1, T2 and
White Matter nulled (WMn) images. Then, a novel Deep learning based strategy
was designed to obtain the automatic segmentations and trained to improve its
robustness and accuaracy using a semisupervised approach. The proposed method
was compared with a related state-of-the-art method showing competitive results
both in terms of segmentation quality and efficiency. To make the proposed
method fully available to the scientific community, a full pipeline able to
work with monomodal standard resolution T1 images is also proposed.
