# DAVIDSTUTZ

07thMAY2017

$D = \frac{2\sum_i^N p_i g_i}{\sum_i^N p_i^2 + \sum_i^N g_i^2}$
where $N$ is the number of voxels, $p_i$ the foreground probability of the prediction and $q_i$ the foreground probability of the ground truth segmentation. As discussed in the paper, the dice loss is differentiable and allows training without assigning weights to the different classes. They provide experimental results on the PROMISE 2012 challenge dataset, see the paper.