Feature-based Visual Odometry for Bronchoscopy: A Dataset and Benchmark

Jianning Deng*, Peize Li, Kevin Dhaliwal, Chris Xiaoxuan Lu, Mohsen Khadem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Bronchoscopy is a medical procedure that involves the insertion of a flexible tube with a camera into the airways to survey, diagnose and treat lung diseases. Due to the complex branching anatomical structure of the bronchial tree and the similarity of the inner surfaces of the segmental airways, navigation systems are now being routinely used to guide the operator during procedures to access the lung periphery. Current navigation systems rely on sensor-integrated bronchoscopes to track the position of the bronchoscope in real-time. This approach has limitations, including increased cost and limited use in non-specialized settings. To address this issue, researchers have proposed visual odometry algorithms to track the bronchoscope camera without the need for external sensors. However, due to the lack of publicly available datasets, limited progress is made. To this end, we have developed a database of bronchoscopy videos in a phantom lung model and ex-vivo human lungs. The dataset contains 34 video sequences with over 23,000 frames with odometry ground truth data collected using electromagnetic tracking sensors. With our dataset, we empower the robotics and machine learning community to advance the field. We share our insights on challenges in endoscopic visual odometry. Furthermore, we provide benchmark results for this dataset. State-of-the-art feature extraction algorithms including SIFT, ORB, Superpoint, Shi-Tomasi, and LoFTR are tested on this dataset. The benchmark results demonstrate that the LoFTR algorithm outperforms other approaches, but still has significant errors in the presence of rapid movements and occlusions.
Original languageEnglish
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages6557-6564
Number of pages8
ISBN (Electronic)978-1-6654-9190-7
ISBN (Print)978-1-6654-9191-4
DOIs
Publication statusPublished - 13 Dec 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: 1 Oct 20235 Oct 2023
https://ieee-iros.org/

Publication series

NameProceedings of the International Conference on Intelligent Robots and Systems
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Abbreviated titleIROS 2023
Country/TerritoryUnited States
CityDetroit
Period1/10/235/10/23
Internet address

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