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.

本文提出了一种利用卷积神经网络进行互动分割编辑的独特训练策略，并通过与现有算法的比较表明其具有卓越的性能，且不仅限于二元分割，可用于形态测量以及效率的提升。