Surgical automation can improve the accessibility and consistency of life saving procedures. Most surgeries require separating layers of tissue to access the surgical site, and suturing to reattach incisions. These tasks involve deformable manipulation to safely identify and alter tissue attachment (boundary) topology. Due to poor visual acuity and frequent occlusions, surgeons tend to carefully manipulate the tissue in ways that enable inference of the tissue's attachment points without causing unsafe tearing. In a similar fashion, we propose JIGGLE, a framework for estimation and interactive sensing of unknown boundary parameters in deformable surgical environments. This framework has two key components: (1) a probabilistic estimation to identify the current attachment points, achieved by integrating a differentiable soft-body simulator with an extended Kalman filter (EKF), and (2) an optimization-based active control pipeline that generates actions to maximize information gain of the tissue attachments, while simultaneously minimizing safety costs. The robustness of our estimation approach is demonstrated through experiments with real animal tissue, where we infer sutured attachment points using stereo endoscope observations. We also demonstrate the capabilities of our method in handling complex topological changes such as cutting and suturing.

翻译：摘要：手术自动化可提升救命手术的可及性与一致性。多数手术需分离组织层以进入手术部位，并通过缝合重新连接切口，这些操作涉及变形操控以安全识别并改变组织连接（边界）拓扑。由于视觉清晰度不足且频繁出现遮挡，外科医生倾向于通过精细操作组织来推断其附着点，同时避免造成不安全撕裂。受此启发，我们提出JIGGLE框架，用于变形手术环境中未知边界参数的估计与交互式感知。该框架包含两个关键组件：（1）概率估计模块，通过融合可微软体仿真器与扩展卡尔曼滤波（EKF）识别当前附着点；（2）基于优化的主动控制流水线，生成可最大化组织附着点信息增益且最小化安全代价的动作序列。通过真实动物组织实验验证了估计方法的鲁棒性——利用立体内窥镜观测推断缝合附着点。我们还展示了该方法处理切割、缝合等复杂拓扑变化的能力。