The field of non-destructive testing (NDT) has been expanded in recent years with many new possibilities. Among other things, new methods of image segmentation (through the use of machine learning or artificial intelligence) and big data scenarios are gaining traction. Together with the ADA Lovelace Center of the Fraunhofer IIS, the German Society for Non-Destructive Testing (DGZFP) brings these two developments together as part of an (international) challenge and find an answer to the question:
"Which automatic or interactive methods from the areas of digital image processing, machine learning or deep Neural networks can segment parts of an old airplane with the highes quality?"
The challenge is divided into several phases (see below). After a training phase in which we provide known pairs of input volumes and there manual annotations, the goal is to segment a dataset for which we will not provide ground truth data. We will then evaluate and compare the results of the submitted segmentations with the manually segmented volumes.