Shape-Based Averaging for Combination of Multiple Segmentations

Combination of multiple segmentations has recently been introduced as an effective method to obtain segmentations that are more accurate than any of the individual input segmentations. This paper introduces a new way to combine multiple segmentations using a novel shape-based averaging method. Individual segmentations are combined based on the signed Euclidean distance maps of the labels in each input segmentation. Compared to label voting, the new combination method produces smoother, more regular output segmentations and avoids fragmentation of contiguous structures. Using publicly available segmented human brain MR images (IBSR database), we perform a quantitative comparison between shape-based averaging and label voting by combining random segmentations with controlled error magnitudes and known ground truth. Shape-based averaging generated combined segmentations that were closer to the ground truth than combinations from label voting for all numbers of input segmentations (up to ten). The relative advantage of shape-based averaging over voting was larger for fewer input segmentations, and larger for greater deviations of the input segmentations from the ground truth. We conclude that shape-based averaging improves the accuracy of combined segmentations, in particular when only a few input segmentations are available and when the quality of the input segmentations is low.