International Journal of Imaging Systems and Technology

Abstract

Quantitative assessment of neuromuscular disorders relies on accurate musculoskeletal ultrasound (MSKUS) image segmentation.However, speckle noise and ambiguous anatomical boundaries pose continuous challenges.Existing convolutional networks often exhibit limitations in balancing detail preservation with noise suppression.Furthermore, traditional multi-scale fusion is prone to transmitting low-level artifacts, causing false positive misclassifications.To address these issues, a robust segmentation network termed CADG-UNet is proposed.The architecture incorporates two tailored modules to process specific acoustic interference.In the encoding path, the Coordinate Attention and Dynamic Spatial Attention (CADSA) module is designed, combining directional positional encoding with content-adaptive dynamic convolutions.This design suppresses spatially heterogeneous noise while precisely anchoring muscle boundaries.In the decoding path, the Dynamic Gated Fusion (DGF) module is designed for skip connections, employing a post-gating local residual refinement mechanism to filter high-frequency reverberation artifacts from shallow features before multi-scale fusion.The proposed method is evaluated on the MUST and DeepACSA datasets.CADG-UNet achieves mean intersection over union (mIoU) scores of 83.31% and 90.97%, respectively.Statistical significance tests indicate stable cross-dataset performance compared to leading baseline methods.Furthermore, the network yields improved boundary and clinical metrics, including reduced 95th percentile Hausdorff Distance (HD95), lower Average Surface Distance (ASD), and cross-sectional area (CSA) estimations.It also demonstrates significant robustness under multiplicative speckle noise conditions.These results suggest that CADG-UNet provides a reliable approach for automated clinical morphological measurements.