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。

Mamba-UNet: 医学图像分割的纯视觉Mamba