Abstract
Accurately modeling and tracking the deformation of soft tissue is critical for a wide range of interventional and surgical procedures. However, current methods struggle in scenarios involving topological changes, such as cutting and dissection, due to the inherent non-linearity and discontinuity introduced by explicit changes in connectivity. In this work, we present a novel, fully differentiable framework that enables robust estimation and modeling of topological changes during deformable tracking. Our method introduces a continuous, sigmoid-based formulation to smooth the otherwise discrete event of tissue cutting, making it amenable to gradient-based optimization within a differentiable Position-Based Dynamics (PBD) simulation. To account for uncertainty and improve robustness in the presence of noisy visual data, we incorporate Stein Variational Gradient Descent (SVGD) for particle-based probabilistic inference, generating multiple hypotheses for topological state estimation. Building on this foundation, we develop an autonomous dissection algorithm for thin-shell tissues that leverages topological updates to guide closed-loop cutting trajectory control. We evaluate our approach in both simulated and real-world electrosurgical environments, demonstrating significant improvements in topological estimation accuracy and dissection precision over existing methods. Our results highlight the potential of this framework to advance automation in soft-tissue surgical procedures by enabling reliable perception and control in the presence of complex structural changes.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Liang, X., Liu, F., Richter, F., Rubaiyat, G., & Yip, M. (2026). ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection. https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection
MLA 9
Liang, Xiao, et al. "ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection." https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection.
Chicago (author–date)
Liang, Xiao, Fei Liu, Florian Richter, Genie Rubaiyat, and Michael Yip. 2026. "ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection." https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection.
Harvard
Liang, X., Liu, F., Richter, F., Rubaiyat, G. and Yip, M. (2026) 'ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection', Available at: https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection.
Vancouver
Liang X, Liu F, Richter F, Rubaiyat G, Yip M. ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection. https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection
IEEE
X. Liang, F. Liu, F. Richter, G. Rubaiyat, and M. Yip, "ProCut: Probabilistic Cutting Topology for Autonomous Electrosurgical Tissue Dissection," https://omanscience.com/en/articles/procut-probabilistic-cutting-topology-for-autonomous-electrosurgical-tissue-dissection.