Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation (2402.05079v2)

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.

Unveiling Mamba-UNet: A Novel Approach for Enhanced Medical Image Segmentation

Introduction

Medical image segmentation plays a pivotal role in diagnostics and therapeutic planning, offering detailed insights into anatomical structures within medical imagery. With deep learning technologies spearheading innovations in this domain, UNet has emerged as a foundational architecture, lauded for its efficiency in handling medical image data through its encoder-decoder framework. However, the quest for architectures capable of capturing intricate details and broader contextual information with greater efficiency has led researchers to explore the integration of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). This exploration aims to overcome the inherent limitations in modeling long-range dependencies, a critical factor in achieving precise segmentation. The proposed Mamba-UNet architecture stands at the confluence of these developments, harnessing the strengths of State Space Models (SSMs) to introduce a novel segmentation approach tailored for medical images.

Architectural Overview

Mamba-UNet distinguishes itself by employing a pure Visual Mamba (VMamba)-based encoder-decoder structure, integrated with skip connections. This design choice enhances the network's capability to learn comprehensive features across different scales, addressing both detailed and broader semantic contexts within medical images. At the heart of Mamba-UNet lies the Visual State Space (VSS) block, optimized for dense data processing and long-range dependency modeling. This shift from conventional vision transformers marks a significant stride towards addressing the computational challenges posed by high-resolution biomedical images.

Encoder and Decoder Design

The architecture intricately designs the encoder and decoder pathways to ensure seamless feature learning and reconstruction, respectively. Incorporating VSS blocks in both pathways allows Mamba-UNet to effectively learn and upscale features, maintaining spatial detail integrity through skip connections. The employment of patch merging and expanding layers further exemplifies the architecture's innovative approach to handling feature resolution and dimensionality, ensuring efficient data flow throughout the network.

Experimental Insights

The empirical evaluation of Mamba-UNet, conducted on the ACDC MRI cardiac segmentation dataset, demonstrates its superior performance over existing UNet and Swin-UNet frameworks under identical hyperparameter settings. Specifically, Mamba-UNet showcases noteworthy improvements in Dice, Intersection over Union (IoU), and other pivotal segmentation metrics, underlying its potential in rendering precise segmentation masks.

Implications and Future Directions

The inception of Mamba-UNet not only sets a new benchmark in medical image segmentation but also opens avenues for future research. Its success emphasizes the efficacy of integrating Visual Mamba blocks within traditional UNet architectures, suggesting a promising direction for advancing segmentation models. Further explorations could extend Mamba-UNet's application across diverse medical imaging modalities and investigative segments, such as 3D medical images and semi/weakly-supervised learning environments, fostering innovation in medical diagnostics and treatment planning.

Conclusion

Mamba-UNet represents a significant advancement in medical image segmentation, transcending traditional boundaries by leveraging the capabilities of Visual Mamba blocks. Its proficient handling of long-range dependencies and computational efficiency, coupled with superior segmentation performance, underscores the potential of integrating SSMs in deep learning architectures for medical imaging. As we forge ahead, the continuous refinement and expansion of Mamba-UNet's application spectrum herald a new era in medical image analysis, driven by AI's transformative power.