Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation

Having ground truth is critical for evaluating segmentation algorithms and estimating the ground truth from a collection of manual segmentations remains a hard problem. A proper estimation approach should take into account and compensate for the inter-rater variation. In this paper, we conduct an analysis of manual segmentations in order to have a better understanding of the pattern of the variation and investigate whether incorporating such pattern information will improve the ground truth estimation. We propose a level-set based approach that solves the ground truth estimation in a probabilistic formulation. The prior pattern information is incorporated into the estimation model by adding a specially designed term in the energy function. Experiments on both synthetic and real data show that this prior information helps to find a more accurate estimate of the ground truth.