XAG-Net通过跨切片注意力提升股骨MRI分割精度
XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation
- 引入像素级跨切片注意力与跳跃门控机制
- 在3个数据集上平均Dice达0.941，优于2D/3D模型
- 适合医学影像分割研究者与临床辅助诊断开发
从磁共振成像（MRI）中精确分割股骨结构对骨科诊断和手术规划至关重要，但现有2D与3D深度学习方法仍存在局限。本文提出XAG-Net，一种基于2.5D U-Net的新型架构，融合像素级跨切片注意力（CSA）与跳跃注意力门控（AG）机制，以增强切片间上下文建模与切片内特征优化。与以往基于CSA的模型不同，XAG-Net在每个空间位置对相邻切片应用像素级softmax注意力，实现细粒度的切片间建模。大量实验表明，XAG-Net在股骨分割精度上超越基线2D、2.5D及3D U-Net模型，同时保持计算效率。消融实验证实了CSA与AG模块的关键作用，确立了其作为高效精准股骨MRI分割框架的潜力。
原文摘要 · Abstract (English)
Accurate segmentation of femur structures from Magnetic Resonance Imaging (MRI) is critical for orthopedic diagnosis and surgical planning but remains challenging due to the limitations of existing 2D and 3D deep learning-based segmentation approaches. In this study, we propose XAG-Net, a novel 2.5D U-Net-based architecture that incorporates pixel-wise cross-slice attention (CSA) and skip attention gating (AG) mechanisms to enhance inter-slice contextual modeling and intra-slice feature refinement. Unlike previous CSA-based models, XAG-Net applies pixel-wise softmax attention across adjacent slices at each spatial location for fine-grained inter-slice modeling. Extensive evaluations demonstrate that XAG-Net surpasses baseline 2D, 2.5D, and 3D U-Net models in femur segmentation accuracy while maintaining computational efficiency. Ablation studies further validate the critical role of the CSA and AG modules, establishing XAG-Net as a promising framework for efficient and accurate femur MRI segmentation.
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