  We present a method that "meta" classifies whether seg-ments predicted by a
semantic segmentation neural networkintersect with the ground truth. For this
purpose, we employ measures of dispersion for predicted pixel-wise class
probability distributions, like classification entropy, that yield heat maps of
the input scene's size. We aggregate these dispersion measures segment-wise and
derive metrics that are well-correlated with the segment-wise IoU of prediction
and ground truth. This procedure yields an almost plug and play post-processing
tool to rate the prediction quality of semantic segmentation networks on
segment level. This is especially relevant for monitoring neural networks in
online applications like automated driving or medical imaging where reliability
is of utmost importance. In our tests, we use publicly available
state-of-the-art networks trained on the Cityscapes dataset and the BraTS2017
dataset and analyze the predictive power of different metrics as well as
different sets of metrics. To this end, we compute logistic LASSO regression
fits for the task of classifying IoU=0 vs. IoU>0 per segment and obtain AUROC
values of up to 91.55%. We complement these tests with linear regression fits
to predict the segment-wise IoU and obtain prediction standard deviations of
down to 0.130 as well as $R^2$ values of up to 84.15%. We show that these
results clearly outperform standard approaches.
