# BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation

arXiv: https://arxiv.org/abs/2204.11982 · HTML: https://mlsanity.com/paper/2204.11982/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking](https://mlsanity.com/paper/1607.05781/) (2016)
- [Autonomous Driving in the Lung using Deep Learning for Localization](https://mlsanity.com/paper/1907.08136/) (2019)
- [OffsetNet: Deep Learning for Localization in the Lung using Rendered Images](https://mlsanity.com/paper/1809.05645/) (2018)
- [Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data](https://mlsanity.com/paper/1709.06041/) (2017)
- [Deep Learning for Localization in the Lung](https://mlsanity.com/paper/1903.10554/) (2019)

## Built on by (most cited first, 9 of 9)

- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy](https://mlsanity.com/paper/2403.01683/) (2024)
- [PANS: Probabilistic Airway Navigation System for Real-time Robust Bronchoscope Localization](https://mlsanity.com/paper/2407.05554/) (2024)
- [Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge](https://mlsanity.com/paper/2307.08318/) (2023)
- [BREATH-VL: Vision-Language-Guided 6-DoF Bronchoscopy Localization via Semantic-Geometric Fusion](https://mlsanity.com/paper/2601.03713/) (2026)
- [Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization](https://mlsanity.com/paper/2505.24249/) (2025)
- [Stop Holding Your Breath: CT-Informed Gaussian Splatting for Dynamic Bronchoscopy](https://mlsanity.com/paper/2604.28179/) (2026)
- [REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning](https://mlsanity.com/paper/2501.18124/) (2025)
- [BronchoTop: Bronchoscopy Navigation via RGB-Only Topological Localization](https://mlsanity.com/paper/2609.28328/) (2026)

## Abstract (arXiv)

Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions. In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.