Learning Probabilistic Fusion of Multilabel Lesion Contours

Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings, primarily in proximity to lesion contours. In this study we address the case where the experts opinion for those ambiguous areas is considered as a distribution over the possible values. We propose a novel method that modifies the experts' distributional opinion at ambiguous areas by fusing their markings based on their sensitivity and specificity. The algorithm can be applied at the end of any label fusion algorithm that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. The method was evaluated on the MICCAI 2016 challenge dataset, and outperformed previous methods.