  The new era of image segmentation leveraging the power of Deep Neural Nets
(DNNs) comes with a price tag: to train a neural network for pixel-wise
segmentation, a large amount of training samples has to be manually labeled on
pixel-precision. In this work, we address this by following an indirect
solution. We build upon the advances of the Explainable AI (XAI) community and
extract a pixel-wise binary segmentation from the output of the Layer-wise
Relevance Propagation (LRP) explaining the decision of a classification
network. We show that we achieve similar results compared to an established
U-Net segmentation architecture, while the generation of the training data is
significantly simplified. The proposed method can be trained in a weakly
supervised fashion, as the training samples must be only labeled on
image-level, at the same time enabling the output of a segmentation mask. This
makes it especially applicable to a wider range of real applications where
tedious pixel-level labelling is often not possible.
