Review of image segmentation applications based on Mamba

Yan Li
Peng Bo
Li Qiang
Li Bo
Lan Xiaoyan
School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang Sichuan 612000, China

Abstract

With the widespread application of deep learning technologies in image segmentation tasks such as medical imaging, remote sensing images, and crack detection, traditional convolutional neural networks and Transformers still have limitations in global modeling and computational efficiency. Mamba, as a new framework based on selective state-space models, demonstrates unique advantages in capturing long-range dependencies and global contextual information due to its linear time complexity, and has gradually gained attention in the field of image segmentation. This study reviews Mamba and the development of its structure, analyzes its current applications in medical imaging, remote sensing, and crack segmentation, and systematically discusses the problems existing in different Mamba-based models under similar scenarios. Finally, it discusses the challenges faced by current research and future developments, providing a reference for subsequent studies.

Foundation Support

国家自然科学基金资助项目(12203039)
等离子体物理全国重点实验室开放课题资助项目(6142A042420506)
涪江实验室资助项目(2023ZYDF074)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0483
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-24] Accepted Paper

Cite This Article

严莉, 彭波, 李强, 等. 基于Mamba的图像分割应用综述 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0483. (Yan Li, Peng Bo, Li Qiang, et al. Review of image segmentation applications based on Mamba [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0483. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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