XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation
This work addresses femur MRI segmentation for orthopedic applications, representing an incremental improvement with novel attention mechanisms.
The paper tackled the challenge of accurately segmenting femur structures from MRI for orthopedic diagnosis and surgical planning by proposing XAG-Net, a 2.5D U-Net-based architecture with cross-slice attention and skip gating, which surpassed baseline models in segmentation accuracy while maintaining computational efficiency.
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.