SiamFSA: Optical Flow-driven Structural-aware Siamese Network for Ultrasound Videos Landmark Tracking

Accurate anatomical landmark tracking within ultrasound video is a crucial analysis task with many clinical applications. However, the non-rigid deformations caused by motion and probe extrusion lead to intra-object semantic and scale variations, resulting in inaccurate landmark tracking. Additionally, the inevitable intrinsic speckle noise and imaging artifacts exacerbate the dissimilarity of targets, further complicating the landmark tracking. In this study, we propose a novel Optical Flow-driven Structural-aware Siamese Network, SiamFSA, for landmark tracking in continuous ultrasound images. This approach implicitly incorporates structure and motion priors into the Siamese model to compensate for the influence of intra-object variations caused by protean tissue deformation. Specifically, we imposed an auxiliary fine-grained heatmap regression branch into the Siamese-based backbone to couple anatomical landmarks with position-sensitive templates for better localization. Moreover, we propose a structural drift correction mechanism to align relative positions across continuous frames guided by the optical flow from the template. This mechanism ensures intra-object semantic similarity of anatomical deformations and facilitates better salient feature extraction under landmark-centered alignment constraints. Meanwhile, a structural prior affine transformation module is designed to optimize the template view for landmark tracking by exploring intra-object scale variations during motion, thereby enhancing foreground semantic perception. Extensive experiments on both public and in-house ultrasound datasets demonstrate that our SiamFSA handles protean anatomical landmark tracking more effectively than other state-of-the-art methods, showing its potential in clinical analysis tasks.