  The release of nnU-Net marked a paradigm shift in 3D medical image
segmentation, demonstrating that a properly configured U-Net architecture could
still achieve state-of-the-art results. Despite this, the pursuit of novel
architectures, and the respective claims of superior performance over the U-Net
baseline, continued. In this study, we demonstrate that many of these recent
claims fail to hold up when scrutinized for common validation shortcomings,
such as the use of inadequate baselines, insufficient datasets, and neglected
computational resources. By meticulously avoiding these pitfalls, we conduct a
thorough and comprehensive benchmarking of current segmentation methods
including CNN-based, Transformer-based, and Mamba-based approaches. In contrast
to current beliefs, we find that the recipe for state-of-the-art performance is
1) employing CNN-based U-Net models, including ResNet and ConvNeXt variants, 2)
using the nnU-Net framework, and 3) scaling models to modern hardware
resources. These results indicate an ongoing innovation bias towards novel
architectures in the field and underscore the need for more stringent
validation standards in the quest for scientific progress.
