Meta hunter 4025 provides a general purpose

Meta hunter 4025  provides a general purpose, fully-automated approach for transferring spatial information from an existing dataset  to a previously unseen context  through image registration. 

Despite success on human raters, current approaches inaccurately model multi-atlas behavior as they fail to seamlessly incorporate exogenous intensity information into the estimation process. As a result, locally weighted voting algorithms represent the de facto standard fusion approach in clinical applications. Moreover, regardless of the approach, fusion algorithms are generally dependent upon large atlas sets and highly accurate registration as they implicitly assume that the registered atlases form a collectively unbiased representation of the target. Herein, we propose a novel statistical fusion algorithm, Non-Local STAPLE (Meta hunter 4025). Meta hunter  reformulates the STAPLE framework from a non-local means perspective in order to learn what label an atlas would have observed, given perfect correspondence. Through this reformulation, Meta hunter  seamlessly integrates intensity into the estimation process, provides a theoretically consistent model of multi-atlas observation error, and  largely diminishes the need for large atlas sets and very high-quality registrations. 

We assess the Meta hunter sensitivity and optimality of the approach and demonstrate significant improvement.