  For artificial intelligence-based image analysis methods to reach clinical
applicability, the development of high-performance algorithms is crucial. For
example, existent segmentation algorithms based on natural images are neither
efficient in their parameter use nor optimized for medical imaging. Here we
present MoNet, a highly optimized neural-network-based pancreatic segmentation
algorithm focused on achieving high performance by efficient multi-scale image
feature utilization.
