Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

TL;DR

Mamba-UNet is a novel medical image segmentation architecture that combines the U-Net framework with the efficient global context modeling capabilities of the Visual Mamba, leading to improved segmentation performance.

Contribution

This paper introduces Mamba-UNet, integrating Visual Mamba blocks into U-Net for better long-range dependency modeling in medical images.

Findings

Outperforms existing UNet variants on MRI and CT segmentation datasets

Effective long-range dependency modeling improves segmentation accuracy

Source code and implementations are publicly available

Abstract

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.