  Semantic segmentation is one of the most popular research areas in medical
image computing. Perhaps surprisingly, despite its conceptualization dating
back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions
for a broad variety of segmentation problems and is regularly used as a
development framework for challenge-winning algorithms. Here we use nnU-Net to
participate in the AMOS2022 challenge, which comes with a unique set of tasks:
not only is the dataset one of the largest ever created and boasts 15 target
structures, but the competition also requires submitted solutions to handle
both MRI and CT scans. Through careful modification of nnU-net's
hyperparameters, the addition of residual connections in the encoder and the
design of a custom postprocessing strategy, we were able to substantially
improve upon the nnU-Net baseline. Our final ensemble achieves Dice scores of
90.13 for Task 1 (CT) and 89.06 for Task 2 (CT+MRI) in a 5-fold
cross-validation on the provided training cases.
