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This paper evaluates strategies to combine multiple segmen- tations of the same image, generated for example by different segmen- tation methods or by different human experts. Three methods are com- pared, each estimating and using a different level of prior knowledge about the segmenters. These three methods are: simple label averag- ing (no priors), a binary expectation maximization (EM) method with independent per-label priors (Warfield et al., MICCAI 2002), and a si- multaneous multi-label EM method with across-label priors (Rohlfing et al., IPMI 2003). The EM methods estimate the accuracies of the individ- ual segmentations with respect to the (unknown) ground truth. These estimates, analogous to expert performance parameters, are then applied as weights in the actual combination step. In the case of the multi-label EM method, typical misclassification behavior, caused for example by neighborhood relationships of different tissues, is also modeled. A valida- tion study using the MNI BrainWeb phantom shows that decision fusion based on the two EM methods consistently outperforms label averag- ing. Of the two EM methods, the multi-label technique produced more accurate combined segmentations than the binary method. We conclude that the EM methods are useful to produce more accurate segmentations from several different segmentations of the same image.

Extraction and Application of Expert Priors to Combine Multiple Segmentations of Human Brain Tissue