G^2RA-NET: Graph-based Cross-Slice Relation Modeling with Attention Gating for Medical Image Segmentation

2026-09-17

AI Summary

本文提出G^2RA-Net，通过图结构跨层关系建模与注意力门控相结合的方法，解决医学图像分割中跨层关系建模效率低的问题，提高了分割准确性和一致性。

Abstract

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.

Problem

• medical image segmentation
• cross-slice relations
• anisotropic volumetric images

Innovation

• Graph-Based Slice Relationship Modeling
• Cross-Slice Attention Gate
• attention-guided feature modulation