[1]马芳芳,赵明月,史亚宁,等.融合视觉状态空间与深度可分离卷积的脑胶质瘤MRI分割方法[J].中国医学物理学杂志,2026,43(6):756-765.[doi:DOI:10.3969/j.issn.1005-202X.2026.06.008]
　MA Fangfang,ZHAO Mingyue,SHI Yaning,et al.Brain glioma MRI segmentation method integrating visual state space and depthwise separable convolution[J].Chinese Journal of Medical Physics,2026,43(6):756-765.[doi:DOI:10.3969/j.issn.1005-202X.2026.06.008]
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融合视觉状态空间与深度可分离卷积的脑胶质瘤MRI分割方法()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第6期
页码:
756-765
栏目:
医学影像物理
出版日期:
2026-06-26

文章信息/Info

Title:
Brain glioma MRI segmentation method integrating visual state space and depthwise separable convolution
文章编号:
1005-202X（2026）06-0756-10
作者:
马芳芳1; 赵明月1; 史亚宁2; 王萍1
1.空军特色医学中心， 北京 100142； 2.陆军军医大学第一附属医院神经外科， 重庆 400038
Author(s):
MA Fangfang1;  ZHAO Mingyue1;  SHI Yaning2;  WANG Ping1
1. Air Force Medical Center, Beijing 100142, China 2. Department of Neurosurgery, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
关键词:
脑胶质瘤; MRI图像分割; VMamba; 视觉状态空间; 深度可分离卷积
Keywords:
Keywords: brain glioma MRI image segmentation VMamba visual state space depthwise separable convolution
分类号:
R318；R739.4
DOI:
DOI:10.3969/j.issn.1005-202X.2026.06.008
文献标志码:
A
摘要:
目的：为提高对脑胶质瘤MRI图像的分割精度，增强对肿瘤全局浸润性及复杂边界的处理能力，提出一种状态空间增强的上下文与边界感知分割网络（SCB-UNet）。方法：首先，在编码器中引入VMamba模块，利用视觉状态空间机制高效建立图像全局依赖关系，以增强模型对肿瘤整体结构的感知能力。其次，在解码器中设计多尺度视觉状态空间前馈网络，通过融合全局上下文与局部多尺度特征，提升模型对肿瘤内部异质成分的区分能力。最后，构建深度可分离卷积扩展模块，在特征上采样前聚合局部上下文信息，以改善复杂边界和小病灶的细节恢复精度。结果：在BraTS2019和BraTS2023公开数据集上的实验结果表明，SCB?Net在不同肿瘤区域上的平均Dice相似系数（DSC）分别达到85.94%和83.33%，平均95% Hausdorff（HD95）距离分别为4.18和5.13 mm。在BraTS2023数据集的消融实验中，相比U-Net基线模型，SCB?Net模型的平均HD95降低1.46 mm。消融实验进一步验证各模块的有效性。结论：SCB?Net模型为脑胶质瘤MRI图像的精准分割提供一种可行的技术途径，具有一定的临床应用潜力。
Abstract:
Abstract: Objective To propose a state space-enhanced context and boundary-aware segmentation network (SCB-UNet) for enhancing the segmentation accuracy of brain glioma MRI images and improving the models ability to cope with the global infiltrative nature and complex boundaries of tumors. Methods A VMamba module was introduced into the encoder to model global image dependencies via a visual state space mechanism, thereby strengthening the models perception of the overall tumor structure. Furthermore, a multi-scale visual state space feedforward network was established in the decoder to integrate global context with local multi-scale features, thus boosting the models ability to distinguish heterogeneous components inside the tumor. Finally, a depthwise separable convolution expansion module is constructed to aggregate local contextual information before feature upsampling, which improves the accuracy of detail recovery for complex boundaries and small lesions. Results Experiments on the public BraTS2019 and BraTS2023 datasets show that the average Dice similarity coefficients of the SCB-UNet across different tumor regions reached 85.94% and 83.33%, respectively, and the average 95% Hausdorff distances were 4.18 and 5.13 mm, respectively. In the ablation experiment on the BraTS2023 dataset, compared with U-Net baseline model, the SCB-UNet reduced the average 95% Hausdorff distance by 1.46 mm, further verifying the effectiveness of each module. Conclusion The SCB-UNet provides a feasible technical solution for the accurate segmentation of brain glioma MRI images and demonstrates promising potential for clinical application.

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备注/Memo

备注/Memo:
【收稿日期】2026-01-05 【基金项目】国家自然科学基金（82173388） 【作者简介】马芳芳，主管技师，研究方向：CT、核磁技术，E-mail: 79159482@qq.com
更新日期/Last Update: 2026-06-26