Autonomous Vision-Guided Resection of Central Airway Obstruction

TL;DR

This paper introduces a vision-guided autonomous system for tracheal tumor resection, demonstrating successful airway clearance in ex-vivo models with high tumor removal efficiency and safety.

Contribution

The study presents a novel autonomous resection platform combining polynomial modeling, deep learning segmentation, and trajectory planning for airway tumor removal.

Findings

Successfully cleared airway obstructions in five ex-vivo experiments

Achieved over 90% volumetric tumor removal

No tracheal perforations occurred during procedures

Abstract

Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool trajectories, while a custom Faster R-CNN segmentation pipeline identifies the trachea and tumor boundaries. The electrocautery tool angle is optimized using handheld surgical demonstrations, and trajectories are planned to maintain a 1 mm safety clearance from the tracheal surface. We validated the workflow successfully in five consecutive experiments on ex-vivo animal tissue models, successfully clearing the airway obstruction without trachea perforation in all cases (with more than 90% volumetric tumor removal). These results support the feasibility of an autonomous resection platform, paving the way for future developments in minimally-invasive autonomous resection.