通过稀疏关键点追踪实现手术中器械与组织交互的概率建模。
Probabilistic Task Parameterization of Tool-Tissue Interaction via Sparse Landmarks Tracking in Robotic Surgery
- 基于稀疏关键点跟踪，动态构建组织局部形变模型。
- 在大幅组织变形下仍能准确传播专家标注的标记点。
- 适合需要理解机器人手术动作的临床研究与算法开发。
机器人手术中精确建模器械-组织交互，需精准追踪可变形组织并融合手术领域知识。传统方法依赖耗时的人工标注或刚性假设，灵活性受限。本文提出一种结合稀疏关键点追踪与概率建模的框架，可在内窥镜视频帧间传播专家标注的地标点，即使面对大范围组织形变亦有效。通过主成分分析（PCA）构建集群组织关键点的动态局部变换，器械位姿也以类似方式相对于这些帧进行追踪。将这些数据嵌入任务参数化高斯混合模型（TP-GMM），融合数据驱动观测与标注的临床经验，有效预测器械与组织的相对位姿，直接从视频数据中提升对机器人手术动作的视觉理解。
原文摘要 · Abstract (English)
Accurate modeling of tool-tissue interactions in robotic surgery requires precise tracking of deformable tissues and integration of surgical domain knowledge. Traditional methods rely on labor-intensive annotations or rigid assumptions, limiting flexibility. We propose a framework combining sparse keypoint tracking and probabilistic modeling that propagates expert-annotated landmarks across endoscopic frames, even with large tissue deformations. Clustered tissue keypoints enable dynamic local transformation construction via PCA, and tool poses, tracked similarly, are expressed relative to these frames. Embedding these into a Task-Parameterized Gaussian Mixture Model (TP-GMM) integrates data-driven observations with labeled clinical expertise, effectively predicting relative tool-tissue poses and enhancing visual understanding of robotic surgical motions directly from video data.
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