3D CT-Video Fusion for Image-Guided Bronchoscopy

Higgins, William E.; Helferty, J.P.; Lü, Kai; Merritt, Scott A.; Rai, Lav; Yu, Kun Chang

doi:10.1016/j.compmedimag.2007.11.001

Cited by 67 publications

(76 citation statements)

References 59 publications

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“…The system described in this paper focuses on procedure planning. Companion works discuss procedure preview, which involves a virtual rehearsal of the procedure plan [18,20], and imageguided bronchoscopy, which entails performing the desired bronchoscopy under guidance from the plan [9,17,19]. "Results" also illustrates the use of a plan for image-guided bronchoscopy.…”

Section: Methodsmentioning

confidence: 99%

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Computer-Based Route-Definition System for Peripheral Bronchoscopy

2011

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Multi-detector computed tomography (MDCT) scanners produce high-resolution images of the chest. Given a patient's MDCT scan, a physician can use an image-guided intervention system to first plan and later perform bronchoscopy to diagnostic sites situated deep in the lung periphery. An accurate definition of complete routes through the airway tree leading to the diagnostic sites, however, is vital for avoiding navigation errors during image-guided bronchoscopy. We present a system for the robust definition of complete airway routes suitable for image-guided bronchoscopy. The system incorporates both automatic and semiautomatic MDCT analysis methods for this purpose. Using an intuitive graphical user interface, the user invokes automatic analysis on a patient's MDCT scan to produce a series of preliminary routes. Next, the user visually inspects each route and quickly corrects the observed route defects using the built-in semiautomatic methods. Application of the system to a human study for the planning and guidance of peripheral bronchoscopy demonstrates the efficacy of the system.

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Section: Methodsmentioning

confidence: 99%

“…The proposed airway route-definition system constitutes part of a large IGI suite we have been developing for the planning and guidance of bronchoscopy [9,[17][18][19][20]. Use of the complete IGI suite involves the following major stages: (a) procedure planning; (b) procedure preview; and (c) live image-guided bronchoscopy.…”

Section: Methodsmentioning

confidence: 99%

Computer-Based Route-Definition System for Peripheral Bronchoscopy

2011

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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%

“…This is especially the case for surgeries that require a very high accuracy due to the risk of damaging healthy tissue (e.g., endonasal skull base surgery [145]). Navigation support is also important for diagnostic procedures like bronchoscopy (examination of the lung airways) where the flexible endoscope has to traverse a complex tree structure with many branches to find the biopsy site that has been identified prior to the examination [48,79]. These pre-operative images or volumetric models are aligned with general information about human anatomy (anatomy atlases) in order to create a patient-specific model that enables a comprehensive and detailed procedure planning.…”

Section: Computer Integrated Surgerymentioning

confidence: 99%

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

Münzer

Schoeffmann

Böszörményi

2017

Multimed Tools Appl

106

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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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“…[4][5][6][7][8][9][10] These methods are based on image registration, and exploit anatomical marks like bifurcations and structural features that characterize the bronchia. Bricault et al 4 proposed a multi-stage tracking algorithm to align optical and virtual images.…”

Section: Introductionmentioning

confidence: 99%

Accurate motion parameter estimation for colonoscopy tracking using a regression method

2010

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Co-located optical and virtual colonoscopy images have the potential to provide important clinical information during routine colonoscopy procedures. In our earlier work, we presented an optical flow based algorithm to compute egomotion from live colonoscopy video, permitting navigation and visualization of the corresponding patient anatomy. In the original algorithm, motion parameters were estimated using the traditional Least Sum of squares(LS) procedure which can be unstable in the context of optical flow vectors with large errors. In the improved algorithm, we use the Least Median of Squares (LMS) method, a robust regression method for motion parameter estimation. Using the LMS method, we iteratively analyze and converge toward the main distribution of the flow vectors, while disregarding outliers. We show through three experiments the improvement in tracking results obtained using the LMS method, in comparison to the LS estimator. The first experiment demonstrates better spatial accuracy in positioning the virtual camera in the sigmoid colon. The second and third experiments demonstrate the robustness of this estimator, resulting in longer tracked sequences: from 300 to 1310 in the ascending colon, and 410 to 1316 in the transverse colon.

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3D CT-Video Fusion for Image-Guided Bronchoscopy

Cited by 67 publications

References 59 publications

Computer-Based Route-Definition System for Peripheral Bronchoscopy

Computer-Based Route-Definition System for Peripheral Bronchoscopy

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

Accurate motion parameter estimation for colonoscopy tracking using a regression method

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