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To evaluate changes in brain structure or function, longitudinal images of brain tumor patients must be non-rigidly registered to account for tissue deformation due to tumor growth or treatment. Most standard non-rigid registration methods will fail to align these images due to the changing feature correspondences between treatment time points and the large deformations near the tumor site. Here we present a registration method which jointly estimates a label map for correspondences to account for the substantial changes that may occur during tumor treatment. Under a Bayesian parameter estimation framework, we employ different probability distributions depending on the correspondence labels. We incorporate models for image similarity, an image intensity prior, label map smoothing, and a transformation prior that encourages deformation near the estimated tumor location. Our proposed algorithm increases registration accuracy compared to a traditional voxel-based registration method as shown using both synthetic and real patient images.