For multi atlas-based segmentation approaches, a segmentation fusion scheme which considers local performance
measures may be more accurate than a method which uses a global performance measure. We improve upon an
existing segmentation fusion method called SIMPLE and extend it to be localized and suitable for multi-labeled
segmentations. We demonstrate the algorithm performance on 23 CT scans of COPD patients using a leave-one-
out experiment. Our algorithm performs significantly better (p < 0.01) than majority voting, STAPLE, and
SIMPLE, with a median overlap of the fissure of 0.45, 0.48, 0.55 and 0.6 for majority voting, STAPLE, SIMPLE,
and the proposed algorithm, respectively.