Non-local STAPLE: An Intensity-Driven Multi-atlas Rater Model

Abstract

Multi-atlas segmentation provides a general purpose, fully automated class of techniques for transferring spatial information from an existing dataset ("atlases") to a previously unseen context ("target") through image registration. The method used to combine information after registration ("label fusion") has a substantial impact on the overall accuracy and robustness. In practice, weighted voting techniques have dramatically outperformed algorithms based on statistical fusion (i.e., algorithms that incorporate rater performance into the estimation process--STAPLE). We posit that a critical limitation of statistical techniques (as generally proposed) is that they fail to incorporate intensity seamlessly into the estimation process and models of observation error. Herein, we propose a novel statistical fusion algorithm, non-local STAPLE, which merges the STAPLE framework with a non-local means perspective. Non-local STAPLE (1) seamlessly integrates intensity into the estimation process, (2) provides a theoretically consistent model of multi-atlas observation error, and (3) largely bypasses the need for group-wise unbiased registrations. We demonstrate significant improvements in two empirical multi-atlas experiments.