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：软组织追踪在计算机辅助介入手术中至关重要。现有方法主要依赖于从模板和视频中提取判别性特征以恢复对应匹配。然而，这些技术难以应用于手术场景，因为组织在手术过程中其形状和外观持续变化。为解决此问题，我们利用光流自然地捕捉像素级的组织形变，并自适应地校正追踪模板。具体而言，我们首先实现一种帧间匹配机制，基于连续帧的光流提取粗略的兴趣区域。为适应外观变化并缓解漂移问题，我们随后提出一种自适应模板匹配方法，该方法根据估计结果的可靠性更新追踪模板。我们的方法Ada-Tracker通过捕捉局部形变实现短期动态建模，并通过引入全局时间补偿实现长期动态建模。我们在公开的SurgT基准测试（该数据集源自Hamlyn、SCARED和Kidney boundary数据集）上评估了我们的方法。实验结果表明，Ada-Tracker实现了卓越的精度，并相较于现有方法表现出更强的鲁棒性。代码可在https://github.com/wrld/Ada-Tracker获取。