  Convolutional Neural Networks (CNNs) and Transformers have been the most
popular architectures for biomedical image segmentation, but both of them have
limited ability to handle long-range dependencies because of inherent locality
or computational complexity. To address this challenge, we introduce U-Mamba, a
general-purpose network for biomedical image segmentation. Inspired by the
State Space Sequence Models (SSMs), a new family of deep sequence models known
for their strong capability in handling long sequences, we design a hybrid
CNN-SSM block that integrates the local feature extraction power of
convolutional layers with the abilities of SSMs for capturing the long-range
dependency. Moreover, U-Mamba enjoys a self-configuring mechanism, allowing it
to automatically adapt to various datasets without manual intervention. We
conduct extensive experiments on four diverse tasks, including the 3D abdominal
organ segmentation in CT and MR images, instrument segmentation in endoscopy
images, and cell segmentation in microscopy images. The results reveal that
U-Mamba outperforms state-of-the-art CNN-based and Transformer-based
segmentation networks across all tasks. This opens new avenues for efficient
long-range dependency modeling in biomedical image analysis. The code, models,
and data are publicly available at https://wanglab.ai/u-mamba.html.
