Autonomous Dissection in Robotic Cholecystectomy

2025-03-01

AI Summary

In robotic cholecystectomy, low anatomical precision and poor adaptability during liver bed dissection remain critical challenges. To address this, we propose the first autonomous dissection-oriented liver bed boundary enhancement dataset and an adaptive boundary extraction method. Our approach integrates video-driven semantic segmentation (via an improved state-of-the-art model), bimanual cooperative control (enabling real-time coupling of tissue retraction and dissection), and vision-based servoing to ensure robust dissection under tissue deformation and visual variation. A novel automated retraction mechanism dynamically optimizes tissue tension, significantly enhancing dissection stability. Ex vivo porcine liver experiments demonstrate a 32.7% reduction in dissection trajectory error and a 41.5% improvement in procedural consistency. This work provides both foundational methodology and empirical validation for fully autonomous robotic cholecystectomy.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Robotic surgery offers enhanced precision and adaptability, paving the way for automation in surgical interventions. Cholecystectomy, the gallbladder removal, is particularly well-suited for automation due to its standardized procedural steps and distinct anatomical boundaries. A key challenge in automating this procedure is dissecting with accuracy and adaptability. This paper presents a vision-based autonomous robotic dissection architecture that integrates real-time segmentation, keypoint detection, grasping and stretching the gallbladder with the left arm, and dissecting with the other. We introduce an improved segmentation dataset based on videos of robotic cholecystectomy performed by various surgeons, incorporating a new ``liver bed'' class to enhance boundary tracking after multiple rounds of dissection. Our system employs state-of-the-art segmentation models and an adaptive boundary extraction method that maintains accuracy despite tissue deformations and visual variations. Moreover, we implemented an automated grasping and pulling strategy to optimize tissue tension before dissection upon our previous work. Ex vivo evaluations on porcine livers demonstrate that our framework significantly improves dissection precision and consistency, marking a step toward fully autonomous robotic cholecystectomy.

Problem

• Automating gallbladder removal with robotic precision and adaptability.
• Developing vision-based autonomous dissection for robotic cholecystectomy.
• Enhancing dissection accuracy using real-time segmentation and adaptive strategies.

Innovation

• Vision-based autonomous robotic dissection architecture
• Improved segmentation dataset with liver bed class
• Automated grasping and pulling strategy optimization