# BronchoLumen: Analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy

arXiv: https://arxiv.org/abs/2605.11748 · HTML: https://mlsanity.com/paper/2605.11748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Autonomous Driving in the Lung using Deep Learning for Localization](https://mlsanity.com/paper/1907.08136/) (2019)
- [BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic Localization](https://mlsanity.com/paper/2402.12763/) (2024)
- [Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy](https://mlsanity.com/paper/2208.11468/) (2022)
- [Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge](https://mlsanity.com/paper/2307.08318/) (2023)
- [BronchoCopilot: Towards Autonomous Robotic Bronchoscopy via Multimodal Reinforcement Learning](https://mlsanity.com/paper/2403.01483/) (2024)
- [BronchoGAN: Anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy](https://mlsanity.com/paper/2507.01387/) (2025)

## Abstract (arXiv)

Bronchoscopy is routinely conducted in pulmonary clinics and intensive care units, but navigating the complex branching of the respiratory tract remains challenging. This paper introduces BronchoLumen, a real-time YOLO-based system for detecting bronchial orifices in video bronchoscopy, aiming to assist navigation and CAD systems. The paper investigates if bronchial orifices can be robustly detected across image domains using state-of-the-art object detection and a limited set of public image data. The study includes the description and comparison of YOLOv8, a widely adopted architecture, and YOLOv12, a more recent architecture integrating attention-based modules to improve spatial reasoning. Both models are trained and tested solely on publicly available datasets comprising different image domains. A comparison of both models is conducted based on the common metrics mAP@0.5 and mAP@0.5:0.9 with the latter emphasizing localization accuracy. For YOLOv8 we obtained a mAP@0.5 of 0.91 on an in-domain and 0.68 on a cross-domain test set. YOLOv12 achieved 0.84 and 0.68 respectively with slightly better localization accuracy with mAP@0.5:0.9 of 0.48 and 0.26 compared to YOLOv8 with 0.45 and 0.25. Challenges like motion blur and low contrast occasionally entailed uncertainties but the system demonstrated overall robustness in most scenarios. BronchoLumen is an open-weight, YOLO-based solution for bronchial orifice detection offering high accuracy and efficiency across multiple image domains. While the more recent YOLOv12 achieves better localization accuracy, we observed a slightly worse precision. The models have been made publicly available to foster further research in bronchoscopy navigation.