  Instance segmentation of compound objects in XXL-CT imagery poses a unique
challenge in non-destructive testing. This complexity arises from the lack of
known reference segmentation labels, limited applicable segmentation tools, as
well as partially degraded image quality. To asses recent advancements in the
field of machine learning-based image segmentation, the "Instance Segmentation
XXL-CT Challenge of a Historic Airplane" was conducted. The challenge aimed to
explore automatic or interactive instance segmentation methods for an efficient
delineation of the different aircraft components, such as screws, rivets, metal
sheets or pressure tubes. We report the organization and outcome of this
challenge and describe the capabilities and limitations of the submitted
segmentation methods.
