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This project aims to improve the performance and reliability of subcortical structure segmentation by combining information from structural and diffusion imaging data. A probabilistic, feature-based model is employed that uses machine learning techniques driven by manual labels, structural intensities and local diffusion-based quantities.  Accurate registration, distortion correction and quantification of uncertainty are crucial elements in this project.