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 MRI cardiac multi-structures segmentation
dataset. The results show that Mamba-UNet outperforms UNet, Swin-UNet in
medical image segmentation under the same hyper-parameter setting. The source
code and baseline implementations are available.

我们提出了一种名为 Mamba-UNet 的创新架构，该架构将 U-Net 在医学图像分割中的能力与 Mamba 的能力相结合，通过纯粹的 Visual Mamba（VMamba）编码器 - 解码器结构和跳跃连接来捕捉医学图像中的细节和上下文，并显著优于具有相同超参数设置的 UNet 和 Swin-UNet。