Label inference with registration and patch priors.

In this paper, we present a novel label inference method that integrates registration and patch priors, and serves as a remedy for labelling errors around structural boundaries. With the initial label map provided by nonrigid registration methods, its corresponding signed distance function can be estimated and used to evaluate the segmentation confidence. The pixels with less confident labels are selected as candidate nodes to be refined and those with relatively confident results are settled as seeds. The affinity between seeds and candidate nodes, which consists of regular image lattice connections, registration prior based on signed distance and patch prior from the warped atlas, is encoded to guide the label inference procedure. For method evaluation, experiments have been carried out on two publicly available data sets and it only takes several seconds for our method to improve the segmentation quality significantly.