Soft tissue tracking is crucial for computer-assisted interventions. Existing approaches mainly rely on extracting discriminative features from the template and videos to recover corresponding matches. However, it is difficult to adopt these techniques in surgical scenes, where tissues are changing in shape and appearance throughout the surgery. To address this problem, we exploit optical flow to naturally capture the pixel-wise tissue deformations and adaptively correct the tracked template. Specifically, we first implement an inter-frame matching mechanism to extract a coarse region of interest based on optical flow from consecutive frames. To accommodate appearance change and alleviate drift, we then propose an adaptive-template matching method, which updates the tracked template based on the reliability of the estimates. Our approach, Ada-Tracker, enjoys both short-term dynamics modeling by capturing local deformations and long-term dynamics modeling by introducing global temporal compensation. We evaluate our approach on the public SurgT benchmark, which is generated from Hamlyn, SCARED, and Kidney boundary datasets. The experimental results show that Ada-Tracker achieves superior accuracy and performs more robustly against prior works. Code is available at https://github.com/wrld/Ada-Tracker.

翻译：软组织跟踪对于计算机辅助介入手术至关重要。现有方法主要依赖从模板与视频中提取判别性特征来恢复对应匹配。然而，在手术场景中组织形态和外观会随手术进程持续变化，这类技术难以直接应用。为解决该问题，我们利用光流自然捕捉像素级组织形变，并自适应修正跟踪模板。具体而言，首先实现帧间匹配机制，基于连续帧的光流提取粗粒度感兴趣区域；为适应外观变化并缓解漂移，进一步提出自适应模板匹配方法，根据估计可靠性动态更新跟踪模板。我们的方法Ada-Tracker通过捕捉局部形变实现短期动态建模，同时引入全局时间补偿实现长期动态建模。在基于Hamlyn、SCARED和肾边界数据集生成的公共SurgT基准上的实验表明，Ada-Tracker相较于现有工作具有更优的准确性和鲁棒性。代码已开源至https://github.com/wrld/Ada-Tracker。