Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation

Accurate cardiac ultrasound segmentation is critical for reliable assessment of ventricular function in intelligent healthcare systems. However, echocardiographic images are inherently challenging due to low contrast, speckle noise, irregular anatomical boundaries, and significant domain shift across acquisition devices and patient populations. Existing methods, primarily driven by appearance-based learning, often struggle to maintain boundary precision and structural consistency under these conditions. To address these limitations, we propose a Contour-Guided Query Refinement Network (CGQR-Net) for boundary-aware cardiac ultrasound segmentation. The proposed framework performs effective information fusion by integrating multi-resolution feature representations with contour-derived structural priors. Specifically, a High-Resolution Network (HRNet) backbone preserves high-resolution spatial information while capturing multi-scale context. A coarse segmentation is first generated, from which anatomical contours are extracted and encoded into learnable query embeddings. These contour-guided queries interact with fused feature maps through cross-attention, enabling structure-aware refinement that enhances boundary delineation and suppresses noise-induced artifacts. In addition, a dual-head supervision strategy jointly optimizes segmentation and boundary predictions to enforce structural consistency. The proposed method is evaluated on the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset and further validated on the CardiacNet dataset to assess cross-dataset generalization. Experimental results demonstrate that CGQR-Net achieves superior segmentation accuracy, improved boundary precision, and strong robustness across different imaging conditions. These findings highlight the effectiveness of integrating contour-level structural information with feature-level representations, providing a robust and generalizable solution for cardiac ultrasound segmentation in real-world clinical and consumer healthcare applications.