Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Luo, Xiangfeng; Feuerstein, Marco; Deguchi, Daisuke; Kitasaka, Takayuki; Takabatake, Hirotsugu; Mori, Kensaku

doi:10.1016/j.media.2010.11.001

Cited by 37 publications

(20 citation statements)

References 38 publications

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“…While a fair, direct comparison between closed-source methods is difficult to achieve and is beyond the scope of this current work, we offer a comparison of results reported in the literature [21], [22]. It is worth noting that such a comparison would best be conducted via multi-institutional collaboration using shared data/phantoms or via open-source/open-dataset so that each method may be applied fairly.…”

Section: Discussionmentioning

confidence: 99%

“…It is worth noting that such a comparison would best be conducted via multi-institutional collaboration using shared data/phantoms or via open-source/open-dataset so that each method may be applied fairly. In Luó et al [21], an analysis of the pose error between an EM tracker and the video-registration was presented, with the EM tracker used as the ground-truth data. A mean translation error was reported between 0.679 and 0.875 mm with orientation error approximately 0.5 °.…”

Section: Discussionmentioning

confidence: 99%

“…Previous work used a conventional tracking system to track the endoscope and navigate within preoperative or intraoperative data [17]–[20], but did not use the video data directly in the registration process. Other related work registers endoscopic video using image-based methods, but registered to preoperative data [21], [22]. …”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a System for High-Accuracy 3D Image-Based Registration of Endoscopic Video to C-Arm Cone-Beam CT for Image-Guided Skull Base Surgery

Mirota

Uneri

Schäfer

et al. 2013

IEEE Trans. Med. Imaging

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The safety of endoscopic skull base surgery can be enhanced by accurate navigation in preoperative computed tomography (CT) or, more recently, intraoperative cone-beam CT (CBCT). The ability to register real-time endoscopic video with CBCT offers an additional advantage by rendering information directly within the visual scene to account for intraoperative anatomical change. However, tracker localization error (~ 1–2 mm) limits the accuracy with which video and tomographic images can be registered. This paper reports the first implementation of image-based video-CBCT registration, conducts a detailed quantitation of the dependence of registration accuracy on system parameters, and demonstrates improvement in registration accuracy achieved by the image-based approach. Performance was evaluated as a function of parameters intrinsic to the image-based approach, including system geometry, CBCT image quality, and computational runtime. Overall system performance was evaluated in a cadaver study simulating transsphenoidal skull base tumor excision. Results demonstrated significant improvement (p < 0.001)in registration accuracy with a mean reprojection distance error of 1.28 mm for the image-based approach versus 1.82 mm for the conventional tracker-based method. Image-based registration was highly robust against CBCT image quality factors of noise and resolution, permitting integration with low-dose intraoperative CBCT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Evaluation of a System for High-Accuracy 3D Image-Based Registration of Endoscopic Video to C-Arm Cone-Beam CT for Image-Guided Skull Base Surgery

Mirota

Uneri

Schäfer

et al. 2013

IEEE Trans. Med. Imaging

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“…For Augmented Reality scenarios where the endoscopic video stream is used as intraoperative modality, various use cases have been addressed in the literature, e.g., registering 3D CT models of the lungs with bronchoscopic videos [79,131]. Similar examples relying on registration are sinus surgery (nose) [31] or skull base surgery [147].…”

Section: Image Registration and Tissue Deformation Trackingmentioning

confidence: 99%

Content-based processing and analysis of endoscopic images and videos: A survey

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

109

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In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Since then, various research communities with manifold backgrounds have picked up on the idea of processing and automatically analyzing the inherently available video signal that is produced by the endoscopic camera. Proposed works mainly include image processing techniques, pattern recognition, machine learning methods and Computer Vision algorithms. While most contributions deal with realtime assistance at procedure time, the post-procedural processing of recorded videos is still in its infancy. Many post-processing problems are based on typical Multimedia methods like indexing, retrieval, summarization and video interaction, but have only been sparsely addressed so far for this domain. The goals of this survey are (1) to introduce this research field to a broader audience in the Multimedia community to stimulate further research, (2) to describe domain-specific characteristics of endoscopic videos that need to be addressed in a pre-processing step, and (3) to systematically bring together the very diverse research results for the first time to provide a broader overview of related research that is currently not perceived as belonging together.

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“…Other closely related work used epipolar geometry for bronchoscope localization in CT [14]. The most closely related work to our own was presented by Luo et al [15] in which a system was developed for bronchoscopic tracking for navigation. Again, the goals of the work are different.…”

mentioning

confidence: 99%

A System for Video-Based Navigation for Endoscopic Endonasal Skull Base Surgery

Mirota

Wang

Taylor

et al. 2012

IEEE Trans. Med. Imaging

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Abstract-Surgeries of the skull base require accuracy to safely navigate the critical anatomy. This is particularly the case for endoscopic endonasal skull base surgery (ESBS) where the surgeons work within millimeters of neurovascular structures at the skull base. Today's navigation systems provide approximately 2 mm accuracy. Accuracy is limited by the indirect relationship of the navigation system, the image and the patient. We propose a method to directly track the position of the endoscope using video data acquired from the endoscope camera. Our method first tracks image feature points in the video and reconstructs the image feature points to produce three-dimensional (3D) points, and then registers the reconstructed point cloud to a surface segmented from pre-operative computed tomography (CT) data. After the initial registration, the system tracks image features and maintains the two-dimensional (2D)-3D correspondence of image features and 3D locations. These data are then used to update the current camera pose. We present a method for validation of our system, which achieves sub-millimeter (0.70 mm mean) target registration error (TRE) results.

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Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Cited by 37 publications

References 38 publications

Evaluation of a System for High-Accuracy 3D Image-Based Registration of Endoscopic Video to C-Arm Cone-Beam CT for Image-Guided Skull Base Surgery

Evaluation of a System for High-Accuracy 3D Image-Based Registration of Endoscopic Video to C-Arm Cone-Beam CT for Image-Guided Skull Base Surgery

Content-based processing and analysis of endoscopic images and videos: A survey

A System for Video-Based Navigation for Endoscopic Endonasal Skull Base Surgery

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