DFF-Net: A Lightweight Dynamic Feature Fusion Network for Instance Segmentation of Axillary Brachial Plexus Ultrasound Images.

The brachial plexus nerves at the axillary level are small in size and have a tortuous course, intertwining with blood vessels and other tissues. Ultrasound images are affected by speckle noise and structural blurring, leading to errors in manual segmentation and reducing the accuracy and success rate of axillary nerve block localization. Current lightweight networks primarily focus on semantic segmentation, making it difficult to precisely distinguish the boundaries between adjacent nerve bundles and surrounding tissues. Additionally, they lack optimisation for axillary nerve instance segmentation, making it challenging to balance accuracy and real-time performance. To address these issues, we propose the Dynamic Feature Fusion Network, which enhances segmentation accuracy while maintaining lightweight architecture. This model combines a re-parameterised vision transformer with spatial pyramid pooling to compress parameter counts while maintaining feature expression capabilities. To achieve dynamic fusion of multi-scale features, this study proposes a SimAM-enhanced Bidirectional Fusion Network based on a similarity attention module, which improves segmentation accuracy in neurovascular intersection regions. Finally, a context-based mechanism is used to improve target localisation accuracy while completing pixel-level segmentation tasks, and a hybrid loss function is designed to enhance the model's localisation accuracy and optimise the training process. The method was evaluated on the Ultrasound Axillary Brachial Plexus dataset and experimental results showed that the proposed method achieved 57FPS and 0.604 mAP@0.5:0.95 on a single GPU. This significantly improves the segmentation accuracy and efficiency of the axillary brachial plexus, providing a reliable auxiliary tool for ultrasound-guided nerve block.