Cheng Wang1, Masahiro Oda2, Yuichiro Hayashi2, Benjamin Villard2, Takayuki Kitasaka3, Hirotsugu Takabatake4, Masaki Mori5, Hirotoshi Honma5, Hiroshi Natori6, Kensaku Mori2. 1. Graduate School of Informatics, Nagoya university, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan. chwang@mori.m.is.nagoya-u.ac.jp. 2. Graduate School of Informatics, Nagoya university, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan. 3. School of Information Science, Aichi Institute of Technology, Yachigusa 1247, Yakusa-cho, Toyota, Aichi, 470-0356, Japan. 4. Department of Respiratory Medicine, Sapporo Minami-Sankyo Hospital, Nishi-6-chome, Minami-3-jo, Chuo-ku, Sapporo, Hokkaido, 060-0063, Japan. 5. Department of Respiratory Medicine, Sapporo-Kosei General Hospital, Higashi-8-chome, Kita-3-jo, Chuo-ku, Sapporo, Hokkaido, 060-0033, Japan. 6. Department of Respiratory Medicine, Keiwakai Nishioka Hospital, 1-52, 4-jo 4-chome, Nishioka, Toyohira-ku, Sapporo, Hokkaido, 062-0034, Japan.
Abstract
PURPOSE: Due to the complex anatomical structure of bronchi and the resembling inner surfaces of airway lumina, bronchoscopic examinations require additional 3D navigational information to assist the physicians. A bronchoscopic navigation system provides the position of the endoscope in CT images with augmented anatomical information. To overcome the shortcomings of previous navigation systems, we propose using a technique known as visual simultaneous localization and mapping (SLAM) to improve bronchoscope tracking in navigation systems. METHODS: We propose an improved version of the visual SLAM algorithm and use it to estimate nt-specific bronchoscopic video as input. We improve the tracking procedure by adding more narrow criteria in feature matching to avoid mismatches. For validation, we collected several trials of bronchoscopic videos with a bronchoscope camera by exploring synthetic rubber bronchus phantoms. We simulated breath by adding periodic force to deform the phantom. We compared the camera positions from visual SLAM with the manually created ground truth of the camera pose. The number of successfully tracked frames was also compared between the original SLAM and the proposed method. RESULTS: We successfully tracked 29,559 frames at a speed of 80 ms per frame. This corresponds to 78.1% of all acquired frames. The average root mean square error for our technique was 3.02 mm, while that for the original was 3.61 mm. CONCLUSION: We present a novel methodology using visual SLAM for bronchoscope tracking. Our experimental results showed that it is feasible to use visual SLAM for the estimation of the bronchoscope camera pose during bronchoscopic navigation. Our proposed method tracked more frames and showed higher accuracy than the original technique did. Future work will include combining the tracking results with virtual bronchoscopy and validation with in vivo cases.
PURPOSE: Due to the complex anatomical structure of bronchi and the resembling inner surfaces of airway lumina, bronchoscopic examinations require additional 3D navigational information to assist the physicians. A bronchoscopic navigation system provides the position of the endoscope in CT images with augmented anatomical information. To overcome the shortcomings of previous navigation systems, we propose using a technique known as visual simultaneous localization and mapping (SLAM) to improve bronchoscope tracking in navigation systems. METHODS: We propose an improved version of the visual SLAM algorithm and use it to estimate nt-specific bronchoscopic video as input. We improve the tracking procedure by adding more narrow criteria in feature matching to avoid mismatches. For validation, we collected several trials of bronchoscopic videos with a bronchoscope camera by exploring synthetic rubber bronchus phantoms. We simulated breath by adding periodic force to deform the phantom. We compared the camera positions from visual SLAM with the manually created ground truth of the camera pose. The number of successfully tracked frames was also compared between the original SLAM and the proposed method. RESULTS: We successfully tracked 29,559 frames at a speed of 80 ms per frame. This corresponds to 78.1% of all acquired frames. The average root mean square error for our technique was 3.02 mm, while that for the original was 3.61 mm. CONCLUSION: We present a novel methodology using visual SLAM for bronchoscope tracking. Our experimental results showed that it is feasible to use visual SLAM for the estimation of the bronchoscope camera pose during bronchoscopic navigation. Our proposed method tracked more frames and showed higher accuracy than the original technique did. Future work will include combining the tracking results with virtual bronchoscopy and validation with in vivo cases.
Entities:
Keywords:
Bronchoscopic navigation; Endoscope tracking; Motion estimation; Visual SLAM