  CNNs, most notably the UNet, are the default architecture for biomedical
segmentation. Transformer-based approaches, such as UNETR, have been proposed
to replace them, benefiting from a global field of view, but suffering from
larger runtimes and higher parameter counts. The recent Vision Mamba
architecture offers a compelling alternative to transformers, also providing a
global field of view, but at higher efficiency. Here, we introduce ViM-UNet, a
novel segmentation architecture based on it and compare it to UNet and UNETR
for two challenging microscopy instance segmentation tasks. We find that it
performs similarly or better than UNet, depending on the task, and outperforms
UNETR while being more efficient. Our code is open source and documented at
https://github.com/constantinpape/torch-em/blob/main/vimunet.md.
