  The U-Net was presented in 2015. With its straight-forward and successful
architecture it quickly evolved to a commonly used benchmark in medical image
segmentation. The adaptation of the U-Net to novel problems, however, comprises
several degrees of freedom regarding the exact architecture, preprocessing,
training and inference. These choices are not independent of each other and
substantially impact the overall performance. The present paper introduces the
nnU-Net ('no-new-Net'), which refers to a robust and self-adapting framework on
the basis of 2D and 3D vanilla U-Nets. We argue the strong case for taking away
superfluous bells and whistles of many proposed network designs and instead
focus on the remaining aspects that make out the performance and
generalizability of a method. We evaluate the nnU-Net in the context of the
Medical Segmentation Decathlon challenge, which measures segmentation
performance in ten disciplines comprising distinct entities, image modalities,
image geometries and dataset sizes, with no manual adjustments between datasets
allowed. At the time of manuscript submission, nnU-Net achieves the highest
mean dice scores across all classes and seven phase 1 tasks (except class 1 in
BrainTumour) in the online leaderboard of the challenge.
