<p>Accurate femoral nerve (FN) segmentation in ultrasound images is crucial for nerve blocks but remains challenging due to its tortuous structure, large-scale variations, and low-contrast boundaries with the closely adjacent femoral artery. Existing methods often yield fragmented segmentations or high false-positive rates due to inadequate long-range dependency modeling and insufficient discriminative feature learning. To address these FN-specific challenges, we propose a Dual-branch Interactive Fusion Network (DIFNet). It employs parallel CNN and Transformer encoders to capture both local details and global context. Specifically, the Cross-Branch Interaction Module (CBIM) bridges semantic gaps and enhances structural continuity; the Multi-Scale Dilated Fusion module (MSDF) handles large-scale variations; and the Region-Guided Enhancement Module (RGEM) refines boundaries and suppresses false positives by explicitly modeling foreground, background, and boundary regions. Experiments demonstrate that DIFNet outperforms state-of-the-art methods on both public (mDice: 90.72%, mIoU: 83.31%) and private datasets (mDice: 91.69%, mIoU: 84.97%), providing more continuous, complete, and accurate segmentations. These results highlight DIFNet’s robustness and effectiveness for femoral nerve segmentation in ultrasound imaging.</p>