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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

3D MRI brain tumor segmentation method based on Mamba-Unet architecture

Zhang Ye
Niu Datian
School of Science, Dalian Minzu University, Dalian Liaoning 116000, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0147

Abstract

Accurate segmentation of multimodal MRI brain tumor images is crucial for clinical diagnosis and prognosis assessment of brain cancer. To address the limitations of convolutional neural networks in capturing global contextual information and modeling long-range dependencies, a novel segmentation model named Polyhedron Conv-Tri-orientated Mamba (PhC-ToMamba) was proposed by integrating Mamba with a U-Net architecture. A Tri-orientated Mamba (ToM) module was embedded in the bottleneck layer to model high-dimensional global features by computing and interacting dependencies along three directions, thereby enhancing global feature representation in 3D medical images. In addition, a novel Polyhedron Convolution (PhConv) was introduced into the encoder to enlarge the receptive field and improve the extraction of critical target regions. These modules effectively enhanced global context awareness and focused attention on key tumor regions. Extensive experiments were conducted on the BraTS 2021 and MSD Task01_BrainTumor datasets. The proposed PhC-ToMamba achieved Dice scores of 95.05% / 90.46%, 94.53% / 89.91%, and 90.74% / 75.91% for whole tumor, tumor core, and enhancing tumor segmentation, respectively. Compared with state-of-the-art methods, PhC-ToMamba demonstrated superior segmentation accuracy and parameter efficiency, providing a robust solution for brain tumor segmentation and improving diagnostic precision.

Foundation Support

国家自然科学基金资助项目(11872145)
辽宁省教育厅基本科研项目(JYTMS20231805)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0147
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-06] Accepted Paper

Cite This Article

张野, 牛大田. 基于Mamba-Unet架构的3DMRI脑肿瘤分割方法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0147. (Zhang Ye, Niu Datian. 3D MRI brain tumor segmentation method based on Mamba-Unet architecture [J]. Application Research of Computers, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0147. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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