Hybrid electromagnetic and image-based tracking of endoscopes with guaranteed smooth output

Reichl, Tobias; Luo, Xiangfeng; Menzel, Manuela; Haŭtmann, Hŭbert; Mori, Kensaku; Navab, Nassir

doi:10.1007/s11548-013-0835-5

Cited by 8 publications

(2 citation statements)

References 51 publications

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“…However, the relative orientation between the camera and the EM sensor will need to be calibrated in order to align the orientation of the camera trajectory and the ground truth trajectory. During our tests, checkerboard-based methods as seen in [35], [36] are very inaccurate. Due to the shallow depth of field of the bronchoscopy camera and its unadjustable focus distance, the camera focuses only on nearby artefacts while visual features outside a few centimetres can appear blurry.…”

Section: Calibrationmentioning

confidence: 98%

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

Deng,

Li,

Dhaliwal

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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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.

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Section: Calibrationmentioning

confidence: 98%

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

Deng,

Li,

Dhaliwal

et al. 2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…To simulate bronchial motion, inherited from previous researches, a dynamic bronchial phantom was employed and with a similar concept, motion model was defined . Hence, the function g can be expressed as a constant velocity motion in the XY plane.…”

Section: Ekf‐slammentioning

confidence: 99%

Compensation of dynamic electromagnetic field distortion using simultaneous localization and mapping method with application in endobronchial ultrasound‐transbronchial needle aspiration (EBUS‐TBNA) guidance

Lavasani

Deevband

Farnia

et al. 2019

Robotics Computer Surgery

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Background: Electromagnetic (EM)-based navigation methods without line-of-sight restrictions may improve lymph node sampling precision in transbronchial needle aspiration (TBNA) procedure. However, EM tracking susceptibility to metallic objects severely declines its precision. Method:We proposed to track the location of a tool in a dynamic bronchial phantom and compensate field distortion in a real-time procedure. Extended Kalman filter simultaneous localization and mapping (EKF-SLAM) algorithm employ the bronchial motion and observations of a redundant sensor. The proposed approach was applied to the phantom with four different amplitudes of breathing motion in the presence of two types of field-distorting objects. Results:The proposed approach improved the accuracy of EM tracking on average from 18.94 ±1.17 mm to 4.59 ±0.29 mm and from 14.2 ±0.69 mm to 4.31 ±0.18mmin the presence of steel and aluminum, respectively. Conclusions:With EM tracking position error reduction based on the EKF-SLAM technique, the approach is appeared promising for a navigated ultrasound TBNA procedure.

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