  Automatic segmentation has great potential to facilitate morphological
measurements while simultaneously increasing efficiency. Nevertheless often
users want to edit the segmentation to their own needs and will need different
tools for this. There has been methods developed to edit segmentations of
automatic methods based on the user input, primarily for binary segmentations.
Here however, we present an unique training strategy for convolutional neural
networks (CNNs) trained on top of an automatic method to enable interactive
segmentation editing that is not limited to binary segmentation. By utilizing a
robot-user during training, we closely mimic realistic use cases to achieve
optimal editing performance. In addition, we show that an increase of the
iterative interactions during the training process up to ten improves the
segmentation editing performance substantially. Furthermore, we compare our
segmentation editing CNN (interCNN) to state-of-the-art interactive
segmentation algorithms and show a superior or on par performance.
