M2GCNet: Multi-Modal Graph Convolution Network for Precise Brain Tumor Segmentation Across Multiple MRI Sequences

Tongxue Zhou
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Abstract

Accurate segmentation of brain tumors across multiple MRI sequences is essential for diagnosis, treatment planning, and clinical decision-making. In this paper, I propose a cutting-edge framework, named multi-modal graph convolution network (M2GCNet), to explore the relationships across different MR modalities, and address the challenge of brain tumor segmentation. The core of M2GCNet is the multi-modal graph convolution module (M2GCM), a pivotal component that represents MR modalities as graphs, with nodes corresponding to image pixels and edges capturing latent relationships between pixels. This graph-based representation enables the effective utilization of both local and global contextual information. Notably, M2GCM comprises two important modules: the spatial-wise graph convolution module (SGCM), adept at capturing extensive spatial dependencies among distinct regions within an image, and the channel-wise graph convolution module (CGCM), dedicated to modelling intricate contextual dependencies among different channels within the image. Additionally, acknowledging the intrinsic correlation present among different MR modalities, a multi-modal correlation loss function is introduced. This novel loss function aims to capture specific nonlinear relationships between correlated modality pairs, enhancing the model’s ability to achieve accurate segmentation results. The experimental evaluation on two brain tumor datasets demonstrates the superiority of the proposed M2GCNet over other state-of-the-art segmentation methods. Furthermore, the proposed method paves the way for improved tumor diagnosis, multi-modal information fusion, and a deeper understanding of brain tumor pathology.
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M2GCNet:多模态图卷积网络用于在多个磁共振成像序列中精确划分脑肿瘤。
在多个核磁共振成像序列中准确分割脑肿瘤对于诊断、治疗计划和临床决策至关重要。在本文中,我提出了一个名为多模态图卷积网络(M2GCNet)的前沿框架,以探索不同磁共振成像模式之间的关系,解决脑肿瘤分割的难题。M2GCNet 的核心是多模态图卷积模块(M2GCM),它是将磁共振模态表示为图的关键组件,节点对应图像像素,边缘捕捉像素之间的潜在关系。这种基于图的表示方法能有效利用局部和全局上下文信息。值得注意的是,M2GCM 包括两个重要模块:空间图卷积模块(SGCM)和通道图卷积模块(CGCM),前者善于捕捉图像中不同区域之间广泛的空间依赖关系,后者则致力于模拟图像中不同通道之间错综复杂的上下文依赖关系。此外,考虑到不同磁共振模式之间的内在相关性,还引入了多模式相关损失函数。这种新颖的损失函数旨在捕捉相关模态对之间的特定非线性关系,从而提高模型获得准确分割结果的能力。在两个脑肿瘤数据集上进行的实验评估表明,所提出的 M2GCNet 优于其他最先进的分割方法。此外,所提出的方法还为改进肿瘤诊断、多模态信息融合以及加深对脑肿瘤病理的理解铺平了道路。
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