发表机构
College of Computer and Information Science, Southwest University; Chengyi College, Jimei University（西南大学计算机与信息科学学院; 集美大学诚毅学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出G^2RA-Net，结合图基跨切片关系建模与注意力门控，提升各向异性医学图像分割的准确性和边界质量。
AI 中文摘要
医学图像分割支持定量临床分析和计算机辅助诊断。最近的医学图像分割方法改进了局部特征表示和体积上下文建模。然而，现有方法在高效建模各向异性体积图像中的跨切片关系方面仍存在困难，限制了分割的一致性和准确性。本文提出G^2RA-Net，一种结合基于图的跨切片关系建模与注意力门控的医学图像分割框架。基于图的切片关系建模（GSRM）通过将每个切片表示为图节点，并通过图消息传递传播语义上下文，捕获连续切片间的解剖依赖关系。跨切片注意力门控（CSAG）随后通过注意力引导的特征调制选择相关的相邻上下文并强调目标解剖区域。在脑部MRI和腹部CT数据集上的实验表明，G^2RA-Net在分割准确性和边界质量方面优于代表性方法。消融研究进一步验证了所提出的设计。
英文摘要
Medical image segmentation supports quantitative clinical analysis and computer-aided diagnosis. Recent methods for medical image segmentation have improved both local feature representation and volumetric context modeling. However, existing methods still strug- gle to efficiently model cross-slice relations in anisotropic volumet- ric images, limiting segmentation consistency and accuracy. This pa- per proposes G^2RA-Net, a medical image segmentation framework that combines graph-based cross-slice relation modeling with atten- tion gating. Graph-Based Slice Relationship Modeling (GSRM) cap- tures anatomical dependencies across consecutive slices by repre- senting each slice as a graph node and propagating semantic con- text through graph message passing. The Cross-Slice Attention Gate (CSAG) then selects relevant neighboring context and emphasizes target anatomical regions through attention-guided feature modula- tion. Experiments on brain MRI and abdominal CT datasets demon- strate that G^2RA-Net outperforms representative methods in seg- mentation accuracy and boundary quality. Ablation studies further validate the proposed design.
Comments5 pages, 6 figures
