Task-Oriented Network Design for Visual Tracking and Motion Filtering of Needle Tip Under 2D Ultrasound

Needle tip tracking under ultrasound (US) imaging is critical for accurate lesion targeting in US-guided percutaneous procedures. While most state-of-the-art trackers have relied on complex network architecture for enhanced performance, the compromised computational efficiency prevents their real-time implementation. Pure visual trackers are also limited in addressing the drift errors caused by temporary needle tip disappearance. In this paper, a compact, task-oriented visual tracker, consisting of an appearance adaptation module and a distractor suppression module, is first designed before it is integrated with a motion filter, namely TransKalman, that leverages the Transformer network for Kalman filter gain estimation. The ablation study shows that the mean tracking success rate (i.e. error <3mm in 95% video frames) of the visual tracker increases by 25% compared with its baseline model. The complete tracking system, integrating the visual tracker and TransKalman, outperforms other existing trackers by at least 5.1% in success rate and 47% in tracking speed during manual needle manipulation experiments in ex-vivo tissue. The proposed real-time tracking system will potentially be integrated in both manual and robotic procedures to reduce operator dependence and improve targeting accuracy during needle-based diagnostic and therapeutic procedures.