SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

By Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024

641 citations recorded by the source corpus.

The Transformer architecture has shown a remarkable ability in modeling global relationships. However, it poses a significant computational challenge when processing high-dimensional medical images. This hinders its development and widespread adoption in this task. Mamba, as a State Space Model (SSM), recently emerged as a notable manner for long-range dependencies in sequential modeling, excelling in natural language processing filed with its remarkable memory efficiency and computational speed. Inspired by its success, we introduce SegMamba, a novel 3D medical image \textbf{Seg}mentation \textbf{Mamba} model, designed to effectively capture long-range dependencies within whole volume features at every scale. Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\times 64\times 64$}. Comprehensive experiments on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our SegMamba. The code for SegMamba is available at: https://github.com/ge-xing/SegMamba

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5358b0e98934f1bbe8f6123a529bbb91dd36d662

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation (2024, methodology): “For medical image segmentation, U-Mamba [56] and SegMamba [88] have proposed task-specific architectures with Mamba blocks respectively based on nn-UNet [32] and Swin UNETR [22], respectively, achieving promising results in various visual tasks and demonstrating the potential of SSMs in vision.”
Gated Linear Attention Transformers with Hardware-Efficient Training (2023, neutral): “…(especially modalities with long-range dependencies), in line with recent work on applying state-of-the-art state-space models to other types of data (Yan et al., 2023; Zhu et al., 2024; Ma et al., 2024; Liu et al., 2024; Xing et al., 2024; Wang et al., 2024a;b; Yang et al., 2024, inter alia ).”
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation (2024, methodology): “Adaptations of these cutting-edge designs to the medical imaging domain have emerged, with claims of superior performance over the conventional CNN-based U-Net [32,11,15,17,41,38,26,9,12,37,34].”
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (2024, methodology): “Different from them, we build a pure-SSM-based model, which can be adopted as a generic vision backbone.”
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, positive): “For medical image segmentation, U-Mamba [18] and SegMamba [29] proposed a task-specific architecture with the Mamba block based on nnU-Net [11] and Swin-UNETR [8], respectively.”
MambaIR: A Simple Baseline for Image Restoration with State-Space Model (2024, background): “Moreover, there are also pioneering works that adopt Mamba to vision tasks such as image classification [44, 92], video understanding [37, 66], biomedical image segmentation [48,71] and others [28,31,56,59,76].”
PointMamba: A Simple State Space Model for Point Cloud Analysis (2024, methodology): “In this work, we focus on designing a simple State Space Model (SSM) for point cloud analysis without attention while maintaining the global modeling advantages of the Transformer.”
LocalMamba: Visual State Space Model with Windowed Selective Scan (2024, positive): “Leveraging Mamba’s foundation in generic models, new methodologies have been developed for visual tasks, such as image segmentation [30, 34, 40, 54] and image synthetic [14], showcasing the adaptability and effectiveness of visual Mamba models in addressing complex vision challenges.”
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation (2024, background): “While Transformers excel in capturing long-range dependencies, their high computational cost, due to the quadratic scaling of the self-attention mechanism with input size, poses a challenge, particularly for high-resolution biomedical images [32,21].”
VM-UNet: Vision Mamba UNet for Medical Image Segmentation (2024, positive): “Additionally, SSM-based models have received substantial research across many fields, including language understanding [13, 15], general vision [23, 50], etc.”