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

Nikhil Uday Shinde, Xiao Liang, Fei Liu, Yutong Zhang, Florian Richter, Sylvia Lee Herbert, Michael C. Yip

Abstract:

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 re-attach 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.