Optimal weights for multi-atlas label fusion.

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.