# Autonomous Vision-Guided Resection of Central Airway Obstruction

arXiv: https://arxiv.org/abs/2502.18586 · HTML: https://mlsanity.com/paper/2502.18586/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SAGE: SLAM with Appearance and Geometry Prior for Endoscopy](https://mlsanity.com/paper/2202.09487/) (2022)

## Built on by (most cited first, 4 of 4)

- [From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction](https://mlsanity.com/paper/2503.16263/) (2025)
- [SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing](https://mlsanity.com/paper/2510.20965/) (2025)
- [PERSEUS: Perception with Semantic Endoscopic Understanding and SLAM](https://mlsanity.com/paper/2509.13541/) (2025)
- [Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy](https://mlsanity.com/paper/2609.16186/) (2026)

## Abstract (arXiv)

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.