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U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2401.04722v1)

Published 9 Jan 2024 in eess.IV, cs.CV, and cs.LG

Abstract: Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational complexity. To address this challenge, we introduce U-Mamba, a general-purpose network for biomedical image segmentation. Inspired by the State Space Sequence Models (SSMs), a new family of deep sequence models known for their strong capability in handling long sequences, we design a hybrid CNN-SSM block that integrates the local feature extraction power of convolutional layers with the abilities of SSMs for capturing the long-range dependency. Moreover, U-Mamba enjoys a self-configuring mechanism, allowing it to automatically adapt to various datasets without manual intervention. We conduct extensive experiments on four diverse tasks, including the 3D abdominal organ segmentation in CT and MR images, instrument segmentation in endoscopy images, and cell segmentation in microscopy images. The results reveal that U-Mamba outperforms state-of-the-art CNN-based and Transformer-based segmentation networks across all tasks. This opens new avenues for efficient long-range dependency modeling in biomedical image analysis. The code, models, and data are publicly available at https://wanglab.ai/u-mamba.html.

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Authors (3)
  1. Jun Ma (347 papers)
  2. Feifei Li (47 papers)
  3. Bo Wang (823 papers)
Citations (204)
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Summary

Overview of U-Mamba for Biomedical Image Segmentation

The research paper introduces U-Mamba, a novel architecture aimed at addressing limitations in modeling long-range dependencies within biomedical image segmentation. Convolutional Neural Networks (CNNs) and Transformers, the current dominant architectures, encounter challenges due to inherent locality and computational complexity, respectively. U-Mamba emerges as a hybrid architecture that leverages both CNNs and State Space Sequence Models (SSMs), notably improving upon existing segmentation networks across tasks involving abdominal CT and MRI scans, endoscopy, and microscopy images.

Architectural Design

U-Mamba integrates the best of CNNs and SSMs, particularly structured state space sequence models like Mamba, to enhance long-range dependency modeling. The architecture is characterized by a hybrid CNN-SSM block, optimizing the extraction of local and global features. A prominent feature of U-Mamba is its self-configuring mechanism, which allows it to adapt automatically to various datasets, a haLLMark inherited from the nnU-Net framework. Furthermore, U-Mamba's design achieves linear scaling with feature size, offering a significant computational advantage over the quadratic complexity characteristic of Transformers.

Experimental Evaluation

The authors conducted extensive experiments across multiple datasets, demonstrating U-Mamba's superior performance. The architecture was benchmarked against several state-of-the-art CNN and Transformer-based networks, including nnU-Net, SegResNet, UNETR, and SwinUNETR. Notable quantitative improvements were observed, with U-Mamba achieving higher Dice Similarity Coefficients (DSC) and Normalized Surface Distances (NSD), particularly in handling abdominal organ segmentation and producing fewer segmentation outliers.

Implications

The promising results of U-Mamba suggest significant implications for the future of biomedical image segmentation. The architecture's ability to efficiently handle long-range dependencies paves the way for its potential application as a foundational backbone in next-generation segmentation tasks. Furthermore, the self-configuring feature aligns U-Mamba for broader application scenarios, providing a flexible solution for diverse biomedical imaging challenges.

Prospects for Future Research

While U-Mamba showcases a novel approach to segmentation, numerous avenues remain open for further exploration. Future research could focus on integrating large-scale datasets to unlock the architecture’s potential in creating deployable segmentation tools. Additionally, exploring U-Mamba within classification and detection networks could further validate its applicability beyond segmentation. The integration of advanced data augmentation techniques and loss functions tailored to specific biomedical applications could also enhance its utility.

In conclusion, U-Mamba represents a strategic enhancement in biomedical image segmentation. Its innovative hybrid architecture offers a compelling solution to the challenges of modeling long-range dependencies, holding promise for widespread impact within the field. The paper’s contributions are significant, setting a new direction for the integration of CNNs and SSMs in image analysis.