GTMamba: Graph Tri-Orientated Mamba Network for 3D Brain Tumor Segmentation

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-05-12 DOI:10.1002/ima.70111

Ye Zhang, Muqing Zhang, Jianxin Zhang, Yangyang Shen, Datian Niu

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引用次数: 0

Abstract

Recently, Mamba has garnered increasing attention due to its efficiency and effectiveness in modeling long-range dependencies. However, adapting it to non-sequential brain tumor image data remains a significant challenge. To address this, we propose the Graph Tri-orientated Mamba network (GTMamba) for brain tumor image segmentation. This network is capable of flexibly capturing the relationships between vertices and their neighboring vertices, thereby enhancing the selection mechanism of the Mamba module. This improvement allows the network to better adapt to non-sequential image data and significantly enhances segmentation accuracy. On the BraTS 2021 and MSD Task01_BrainTumour datasets, GTMamba achieved Dice values of 94.29%/92.08%, 94.01%/90.58%, and 88.44%/74.02% for the whole tumor, tumor core, and enhanced tumor segmentation tasks, respectively. Compared to other state-of-the-art methods, GTMamba demonstrates superior overall performance in terms of segmentation accuracy and parameter efficiency.

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GTMamba：三维脑肿瘤分割的图三取向曼巴网络

最近，Mamba由于其在建模远程依赖关系方面的效率和有效性而获得了越来越多的关注。然而，将其适应于非顺序脑肿瘤图像数据仍然是一个重大挑战。为了解决这一问题，我们提出了用于脑肿瘤图像分割的图三面向曼巴网络（GTMamba）。该网络能够灵活地捕获顶点与相邻顶点之间的关系，从而增强了Mamba模块的选择机制。这种改进使网络能够更好地适应非序列图像数据，显著提高分割精度。在BraTS 2021和MSD task01_braintumor数据集上，GTMamba在整个肿瘤、肿瘤核心和增强肿瘤分割任务上的Dice值分别为94.29%/92.08%、94.01%/90.58%和88.44%/74.02%。与其他先进的方法相比，GTMamba在分割精度和参数效率方面表现出优越的整体性能。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.