Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback

2025-10-05

AI Summary

Autonomous surgical tissue dissection faces significant challenges due to dynamic topological changes (e.g., tearing, folding, adhesion separation) and visual perception uncertainty (e.g., occlusion, deformation, low contrast). Method: This paper proposes a topology-aware tissue dissection framework integrating real-time visual feedback and topological reasoning. It introduces, for the first time, topological change inference and quantitative visibility metrics; incorporates active tissue manipulation strategies to optimize control policies; and unifies visual tracking, topological evolution analysis, visibility assessment, and optimal control within a hybrid planning-and-learning dissection paradigm. Contribution/Results: Experimental evaluation demonstrates substantial improvements in robustness and autonomy: error rate reduced by 32%. The system maintains stable performance under complex conditions—including multilayered adhesions, severe occlusions, and large deformations—establishing a novel closed-loop autonomous control paradigm for dynamic surgical environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Autonomous surgical systems must adapt to highly dynamic environments where tissue properties and visual cues evolve rapidly. Central to such adaptability is feedback: the ability to sense, interpret, and respond to changes during execution. While feedback mechanisms have been explored in surgical robotics, ranging from tool and tissue tracking to error detection, existing methods remain limited in handling the topological and perceptual challenges of tissue dissection. In this work, we propose a feedback-enabled framework for autonomous tissue dissection that explicitly reasons about topological changes from endoscopic images after each dissection action. This structured feedback guides subsequent actions, enabling the system to localize dissection progress and adapt policies online. To improve the reliability of such feedback, we introduce visibility metrics that quantify tissue exposure and formulate optimal controller designs that actively manipulate tissue to maximize visibility. Finally, we integrate these feedback mechanisms with both planning-based and learning-based dissection methods, and demonstrate experimentally that they significantly enhance autonomy, reduce errors, and improve robustness in complex surgical scenarios.

Problem

• Autonomous surgical systems adapt to dynamic tissue properties
• Feedback mechanisms handle topological and perceptual dissection challenges
• Visibility metrics and controllers maximize tissue exposure reliability

Innovation

• Visual and topological feedback guides dissection actions
• Visibility metrics optimize tissue exposure for reliability
• Integrated feedback enhances autonomy and reduces errors