In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Research on multi-module collaborative brain tumor MRI segmentation method based on improved transunet

Shen Haiyun
Xiang Haorui
Deng Zhouyao
School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0193

Abstract

To address the challenges of limited multi-scale feature extraction capability, boundary blurring, and spatial-semantic fragmentation caused by modal differences in brain tumor MRI image segmentation, this paper proposes an improved brain tumor segmentation model named CDSTransUnet. Firstly, the cross-guided multi-scale attention interaction module (CMAM) is designed to enhance multi-scale feature capture through a synergistic optimization mechanism combining dynamic channel attention and deformable spatial attention, compensating for the scale perception limitations of the Transformer. Secondly, the dual-pooling edge enhancement module (DPEEM) is proposed to extract residual edge features using a hybrid pooling difference strategy, effectively strengthening tumor boundary recognition and segmentation capability. Thirdly, the spatial-semantic fusion gating mechanism (SSFG) is constructed to alleviate the heterogeneous feature misalignment problem by dynamically fusing the local spatial features of the CNN and the global semantic information of the Transformer via adaptive weights. Evaluated on the BraTS2020 and BraTS2019 datasets, the proposed method achieved Dice coefficients of 89.19%/88.60%, 83.58%/82.19%, and 75.89%/76.86% for the tumor core (TC) , whole tumor (WT) , and enhancing tumor (ET) regions, respectively. This represents a relative improvement of 2.64%/1.74%, 4.81%/3.98%, and 2.16%/1.90% over the baseline model on the BraTS2020/BraTS2019 datasets. Furthermore, the Hausdorff distance (HD) was significantly reduced. Experiments verify the effectiveness and robustness of the model in segmenting complex tumor morphology, providing reliable support for medical image analysis.

Foundation Support

南充市-西南石油大学市校科技战略合作项目(23XNSYSX0106、23XNSYJG0051)
智能电网与智能控制南充市重点实验(NCKL201918)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0193
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-16] Accepted Paper

Cite This Article

谌海云, 向浩睿, 邓洲垚. 基于改进TransUnet的多模块协同脑肿瘤MRI分割方法研究 [J]. 计算机应用研究, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0193. (Shen Haiyun, Xiang Haorui, Deng Zhouyao. Research on multi-module collaborative brain tumor MRI segmentation method based on improved transunet [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0193. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)