CUSTrack: Causality-Inspired Liver Ultrasound Tracking With Periodic Motion Bias Mitigation

Real-time tissue tracking is a fundamental task in liver ultrasound applications. Due to the periodic nature of liver motion, historical trajectories can offer valuable priors for target localization, particularly when foreground-background distinction is weak. However, existing trackers often exploit these trajectories as shortcuts, relying excessively on periodic respiratory patterns rather than true object appearance matching. In this work, we revisit liver tracking from a causal perspective and propose CUSTrack, a method that mitigates periodicity bias by decomposing and correcting the total causal effect of historical trajectories. We define periodicity bias as the direct causal effect of past states and eliminate it via counterfactual reasoning, preserving 'good' trajectory priors while suppressing 'bad' periodic bias. To ensure identifiability, we incorporate a deconfounding module that removes latent confounders from fused feature representations. Extensive experiments on liver ultrasound datasets demonstrate that CUSTrack achieves superior tracking accuracy and robustness under challenging conditions.