BoneContourNet-Vis: a lightweight end-to-end deep learning framework for real-time ultrasound bone imaging in...

Real-time ultrasound bone visualization is crucial for orthopedic surgical navigation, but current methods rely on a two-stage "segmentation-then-rendering" pipeline that adds latency and separates the enhancement from the original image context. These methods also struggle with discontinuous bone contours due to acoustic shadowing and angle sensitivity, limiting their clinical utility. We aimed to develop a new end-to-end deep learning framework to overcome these challenges and provide real-time, continuous bone visualization.

We reformulated ultrasound bone visualization as a direct guided overlay task: a deep network predicts a calibrated bone probability map and fuses it with the ultrasound image, eliminating separate mask rendering. Based on this concept, we developed BoneContourNet-Vis, a lightweight end-to-end model built on a ConvNeXt V2 Nano backbone for strong feature extraction and high-throughput inference. The network incorporates three specialized modules: an Edge Attention Module (EAM) to enhance thin cortical edges, a Physics-aware Interaction Module (PIM) to inject acoustic shadow and phase priors into deep features, and a Contour-Adaptive Module (CAM) to enforce smooth, continuous bone contours via graph-based refinement.

Comprehensive evaluation on the Bone100K dataset (∼100k ultrasound frames) demonstrated that our method outperforms representative approaches (e.g., HiFormer, MedNeXt, MedSAM) with Dice 0.933, IoU 0.875, Precision 0.96, Recall 0.91, Specificity 0.95, HD95 6.52 px, ASSD 2.07 px, while achieving ∼109 frames per second (9.18 ms latency) - meeting intraoperative real-time requirements. Our approach showed more complete and continuous bone contours under heavy acoustic shadowing and maintained the grayscale context of the original ultrasound.

The proposed BoneContourNet-Vis framework improves the completeness and interpretability of ultrasound bone imaging without sacrificing inference speed. It delivers an accurate and real-time bone visualization solution. In future work, this framework can be extended to multi-planar or dynamic ultrasound sequences to achieve real-time three-dimensional bone reconstruction. Combined with optical tracking and probe calibration, the method can further enhance millimeter-level localization accuracy, thereby providing robust technical support for clinical orthopedic navigation, intraoperative guidance, and rapid bedside fracture assessment.