用Mamba设计追踪器，解决超声引导穿刺时针体快速往复运动的追踪难题。
MrTrack: Register Mamba for Needle Tracking with Rapid Reciprocating Motion during Ultrasound-Guided Aspiration Biopsy
- 基于Mamba的注册机制，逐帧提取历史搜索图的全局上下文信息。
- 在针体运动导致图像模糊时，从注册库中调取外部提示，保持追踪连续性。
- 自监督多样性损失防止特征坍缩，适合临床超声穿刺实时追踪场景。
超声引导细针穿刺活检是一种常见微创诊断方法。然而，针对针体快速往复运动的追踪器仍存在空白。本文提出MrTrack，一种基于Mamba的注册式穿刺针追踪方法。MrTrack利用Mamba-based注册提取器，从每帧历史搜索图中顺序提取全局上下文，并将这些时序线索存储于注册库中。当因快速往复运动和成像退化导致当前视觉特征暂时不可用时，Mamba-based注册检索器会从注册库中提取时序提示作为外部引导。同时，提出自监督注册多样化损失，促进注册表征中的特征多样性与维度独立性，缓解特征坍缩问题。在机器人与人工穿刺活检数据集上的全面实验表明，MrTrack不仅在精度与鲁棒性上优于现有最先进追踪方法，且推理效率更优。
原文摘要 · Abstract (English)
Ultrasound-guided fine needle aspiration (FNA) biopsy is a common minimally invasive diagnostic procedure. However, an aspiration needle tracker addressing rapid reciprocating motion is still missing. MrTrack, an aspiration needle tracker with a mamba-based register mechanism, is proposed. MrTrack leverages a Mamba-based register extractor to sequentially distill global context from each historical search map, storing these temporal cues in a register bank. The Mamba-based register retriever then retrieves temporal prompts from the register bank to provide external cues when current vision features are temporarily unusable due to rapid reciprocating motion and imaging degradation. A self-supervised register diversify loss is proposed to encourage feature diversity and dimension independence within the learned register, mitigating feature collapse. Comprehensive experiments conducted on both robotic and manual aspiration biopsy datasets demonstrate that MrTrack not only outperforms state-of-the-art trackers in accuracy and robustness but also achieves superior inference efficiency. Project page: https://github.com/PieceZhang/MrTrack
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