JIGGLE: An Active Sensing Framework for Boundary Parameters Estimation in Deformable Surgical Environments

TL;DR

JIGGLE is a novel active sensing framework that estimates boundary parameters in deformable surgical tissues by combining probabilistic estimation with active control, improving surgical automation and safety.

Contribution

It introduces a new framework integrating a differentiable soft-body simulator with EKF and active control for boundary estimation in deformable tissues, handling complex topological changes.

Findings

Successfully infers sutured attachment points from stereo endoscope data.

Demonstrates robustness in handling tissue cutting and suturing.

Validated on real animal tissue experiments.

Abstract

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.