MambaXCTrack: Mamba-based Tracker with SSM Cross-correlation and Motion Prompt for Ultrasound Needle Tracking

2024-11-13

arXiv.org

AI Summary

In ultrasound-guided needle insertion, needle tip visibility is intermittent and localization unstable due to imaging noise, artifacts, and the needle’s thin in-plane profile. This paper proposes the first real-time needle tip tracker based on the Mamba architecture. Our method addresses these challenges via two key innovations: (1) the first integration of Structured State Space Models (SSMs) into ultrasound needle tracking, realized through a cross-frame correlation module (SSMX-Corr) that enables long-range semantic matching and global search; and (2) an implicit low-level motion descriptor serving as a non-visual cue to mitigate tracking failure during transient needle tip occlusion. Evaluated on motor-driven phantom and ex vivo tissue datasets, our approach significantly outperforms state-of-the-art methods. Ablation studies confirm the critical contributions of SSMX-Corr and the implicit motion descriptor to both accuracy and robustness.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US image presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker MambaXCTrack utilizing structured state space models cross-correlation (SSMX-Corr) and implicit motion prompt is proposed, which is the first application of Mamba in US needle tracking. The SSMX-Corr enhances cross-correlation by long-range modeling and global searching of distant semantic features between template and search maps, benefiting the tracking under noise and artifacts by implicitly learning potential distant semantic cues. By combining with cross-map interleaved scan (CIS), local pixel-wise interaction with positional inductive bias can also be introduced to SSMX-Corr. The implicit low-level motion descriptor is proposed as a non-visual prompt to enhance tracking robustness, addressing the intermittent tip visibility problem. Extensive experiments on a dataset with motorized needle insertion in both phantom and tissue samples demonstrate that the proposed tracker outperforms other state-of-the-art trackers while ablation studies further highlight the effectiveness of each proposed tracking module.

Problem

• Tracking needle tip in noisy ultrasound images
• Enhancing cross-correlation with long-range semantic features
• Addressing intermittent visibility of needle tip

Innovation

• Mamba-based tracker with SSM cross-correlation
• Implicit motion prompt for robustness
• Cross-map interleaved scan for local interaction