  In recent advancements in medical image analysis, Convolutional Neural
Networks (CNN) and Vision Transformers (ViT) have set significant benchmarks.
While the former excels in capturing local features through its convolution
operations, the latter achieves remarkable global context understanding by
leveraging self-attention mechanisms. However, both architectures exhibit
limitations in efficiently modeling long-range dependencies within medical
images, which is a critical aspect for precise segmentation. Inspired by the
Mamba architecture, known for its proficiency in handling long sequences and
global contextual information with enhanced computational efficiency as a State
Space Model (SSM), we propose Mamba-UNet, a novel architecture that synergizes
the U-Net in medical image segmentation with Mamba's capability. Mamba-UNet
adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused
with skip connections to preserve spatial information across different scales
of the network. This design facilitates a comprehensive feature learning
process, capturing intricate details and broader semantic contexts within
medical images. We introduce a novel integration mechanism within the VMamba
blocks to ensure seamless connectivity and information flow between the encoder
and decoder paths, enhancing the segmentation performance. We conducted
experiments on publicly available ACDC MRI Cardiac segmentation dataset, and
Synapse CT Abdomen segmentation dataset. The results show that Mamba-UNet
outperforms several types of UNet in medical image segmentation under the same
hyper-parameter setting. The source code and baseline implementations are
available.
