H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation (2403.13642v1)

Abstract: In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied. However, CNNs are often limited in their ability to deal with long sequences of information, while the low sensitivity of ViTs to local feature information and the problem of secondary computational complexity limit their development. Recently, the emergence of state-space models (SSMs), especially 2D-selective-scan (SS2D), has had an impact on the longtime dominance of traditional CNNs and ViTs as the foundational modules of visual neural networks. In this paper, we extend the adaptability of SS2D by proposing a High-order Vision Mamba UNet (H-vmunet) for medical image segmentation. Among them, the proposed High-order 2D-selective-scan (H-SS2D) progressively reduces the introduction of redundant information during SS2D operations through higher-order interactions. In addition, the proposed Local-SS2D module improves the learning ability of local features of SS2D at each order of interaction. We conducted comparison and ablation experiments on three publicly available medical image datasets (ISIC2017, Spleen, and CVC-ClinicDB), and the results all demonstrate the strong competitiveness of H-vmunet in medical image segmentation tasks. The code is available from https://github.com/wurenkai/H-vmunet .

• The paper introduced H-vmunet, which integrates high-order 2D-selective-scan within a UNet framework for enhanced medical image segmentation.
• It leverages state-space modeling to capture both global and local features, significantly improving Dice Similarity Coefficient across multiple datasets.
• The study demonstrates a 67.28% reduction in parameters compared to prior models, emphasizing its computational efficiency and clinical potential.

High-Order Vision Mamba UNet for Medical Image Segmentation

The paper "H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation" presents a novel approach to enhance the efficacy of medical image segmentation by integrating State-Space Models (SSMs) and High-order 2D-selective-scan (H-SS2D) with the UNet framework. This research addresses existing limitations in Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for image segmentation, where CNNs struggle with long-range dependencies and ViTs have high computational complexity and memory usage.

Proposed Methodology

The core innovation of this paper is the introduction of the High-order Vision Mamba UNet (H-vmunet). This architecture incorporates H-SS2D, a novel extension of the 2D-selective-scan (SS2D) operation used to minimize redundant information while maintaining a wide receptive field. The H-SS2D operation is implemented within a High-order Visual State Space (H-VSS) module, which integrates state-space modeling techniques to ensure efficient feature extraction across multiple layers.

The H-vmunet maintains the U-shaped architecture typical of UNet models, comprising an encoder, decoder, and skip connections that preserve spatial information crucial for medical segmentation. By replacing traditional convolutional modules with the H-VSS module, the proposed model can leverage both global and local feature extraction capabilities, enhancing the segmentation of complex medical images that contain subtle lesion details.

Experimental Results

The effectiveness of H-vmunet was demonstrated through extensive experiments on three publicly available medical image datasets: ISIC2017, Spleen, and CVC-ClinicDB. The results showed a significant improvement in segmentation accuracy compared to existing state-of-the-art models, including several UNet variants and Transformer-based architectures.

Numerically, the proposed H-vmunet model outperformed others with an increase in Dice Similarity Coefficient (DSC) across all datasets, highlighting its superior ability to capture fine-grained details and effectively suppress irrelevant information. Moreover, the model demonstrated a 67.28% reduction in parameters compared to the traditional Vision Mamba UNet (VM-UNet), showcasing its efficiency in handling computational resources.

Implications and Future Work

The introduction of H-vmunet holds substantial implications for the field of medical image segmentation. By effectively balancing computational efficiency with segmentation accuracy, this work paves the way for the deployment of more responsive and resource-conscious medical image analysis applications, which are vital in real-world clinical environments.

Theoretically, this research expands the applicability of state-space models in visual processing tasks, suggesting a potential paradigm shift from traditional CNNs and ViTs to more memory-efficient models capable of handling higher-order interactions. The promising results also speculate on the future utility of such models in other image-intensive tasks beyond medical segmentation.

Future research could explore the application of H-vmunet in various medical contexts, considering diverse imaging modalities and integrating domain-specific knowledge to enhance model robustness. Additionally, investigating the integration of H-vmunet with other innovative architectures and techniques could further refine its performance and applicability. This includes exploring adaptive mechanisms for dynamically adjusting the order of spatial interactions based on the complexity and context of input images.

In conclusion, the paper provides a comprehensive exploration of advancing medical image segmentation through high-order interactions, presenting a model that is both computationally efficient and highly effective, offering a valuable contribution to the continual development of deep learning in medical contexts.