  For complex segmentation tasks, fully automatic systems are inherently
limited in their achievable accuracy for extracting relevant objects.
Especially in cases where only few data sets need to be processed for a highly
accurate result, semi-automatic segmentation techniques exhibit a clear benefit
for the user. One area of application is medical image processing during an
intervention for a single patient. We propose a learning-based cooperative
segmentation approach which includes the computing entity as well as the user
into the task. Our system builds upon a state-of-the-art fully convolutional
artificial neural network (FCN) as well as an active user model for training.
During the segmentation process, a user of the trained system can iteratively
add additional hints in form of pictorial scribbles as seed points into the FCN
system to achieve an interactive and precise segmentation result. The
segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches
can yield superior results compared to networks without the user input channel
component, due to a consistent improvement in segmentation quality after each
interaction.
