  Multi-atlas segmentation (MAS) is a popular image segmentation technique for
medical images. In this work, we improve the performance of MAS by correcting
registration errors before label fusion. Specifically, we use a volumetric
displacement field to refine registrations based on image anatomical appearance
and predicted labels. We show the influence of the initial spatial alignment as
well as the beneficial effect of using label information for MAS performance.
Experiments demonstrate that the proposed refinement approach improves MAS
performance on a 3D magnetic resonance dataset of the knee.
