  This paper explores the use of a soft ground-truth mask ("soft mask'') to
train a Fully Convolutional Neural Network (FCNN) for segmentation of Multiple
Sclerosis (MS) lesions. Detection and segmentation of MS lesions is a complex
task largely due to the extreme unbalanced data, with very small number of
lesion pixels that can be used for training. Utilizing the anatomical knowledge
that the lesion surrounding pixels may also include some lesion level
information, we suggest to increase the data set of the lesion class with
neighboring pixel data - with a reduced confidence weight. A soft mask is
constructed by morphological dilation of the binary segmentation mask provided
by a given expert, where expert-marked voxels receive label 1 and voxels of the
dilated region are assigned a soft label. In the methodology proposed, the FCNN
is trained using the soft mask. On the ISBI 2015 challenge dataset, this is
shown to provide a better precision-recall tradeoff and to achieve a higher
average Dice similarity coefficient. We also show that by using this soft mask
scheme we can improve the network segmentation performance when compared to a
second independent expert.
