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For complex segmentation tasks, fully automatic systems are inherentlylimited in their achievable accuracy for extracting relevant objects.Especially in cases where only few data sets need to be processed for a highlyaccurate result, semi-automatic segmentation techniques exhibit a clear benefitfor the user. One area of application is medical image processing during anintervention for a single patient. We propose a learning-based cooperativesegmentation approach which includes the computing entity as well as the userinto the task. Our system builds upon a state-of-the-art fully convolutionalartificial neural network (FCN) as well as an active user model for training.During the segmentation process, a user of the trained system can iterativelyadd additional hints in form of pictorial scribbles as seed points into the FCNsystem to achieve an interactive and precise segmentation result. Thesegmentation quality of interactive FCNs is evaluated. Iterative FCN approachescan yield superior results compared to networks without the user input channelcomponent, due to a consistent improvement in segmentation quality after eachinteraction.

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model