  Supervised machine learning algorithms, especially in the medical domain, are
affected by considerable ambiguity in expert markings. In this study we address
the case where the experts' opinion is obtained as a distribution over the
possible values. We propose a soft version of the STAPLE algorithm for experts'
markings fusion 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. We suggest that these voxels contain additional anatomical information
about the lesion structure. The fused masks are utilized as ground truth mask
to train a Fully Convolutional Neural Network (FCNN). The proposed method was
evaluated on the MICCAI 2016 challenge dataset, and yields improved
precision-recall tradeoff and a higher average Dice similarity coefficient.
