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We propose a general framework for segmentation of subcortical structures and the hippocampus in magnetic resonance brain images based on multi-atlas label propagation and graph cuts. The label maps obtained from multi-atlas segmentation are used to build a subject-specific probabilistic atlas of a structure of interest. From this atlas and an intensity model estimated from the unseen image, a Markov random field-based energy function is defined and optimized via graph cuts. Compared to a previously proposed approach, our method does not rely on manual training of the intensity model and is applied to five subcortical structures and the hippocampus. We used this approach to segment the hippocampus on 60 images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and achieved an average overlap (Dice coefficient) of 0.86 with the manually delineated reference segmentations.