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Fast, automated segmentation of the thalamic nuclei in the brain has long been desired as it provides for direct visualization of the target for certain procedures like Deep Brain Stimulation (DBS) that target a specific nucleus. It is also beneficial in the study of other pathologies that pertain to different nuclei. In this thesis, a novel approach to fast automated segmentation of thalamic nuclei called Shortened Template and THalamus for Optimal Multi Atlas Segmentation (ST THOMAS) was developed using the multi-atlas segmentation approach. It was designed with a focus on robustness and speed by making use of an averaged template for registration and cropping the inputs and the template. The performance of ST THOMAS was first evaluated on 7T MRI data by comparing with manual delineation (ground truth) by an expert neuroradiologist. Dice coefficients and Volumetric Similarity Indices were used as metrics. To extend the applicability of this method, 3T MRI data were also evaluated. Finally, applications to real time ventralintermideiate (VIM) nucleus targeting for DBS and study of the effects of alcoholism are demonstrated.