Nonrigid 2-D/3-D Registration for Patient Specific Bronchoscopy Simulation With Statistical Shape Modeling: Phantom Validation

Deligianni, Fani; Chung, Adrian J.; Yang, Guang‐Zhong

doi:10.1109/tmi.2006.883452

Cited by 37 publications

(19 citation statements)

References 28 publications

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“…Helferty and Higgins (2001) proposed a method for bronchoscope tracking using image registration based on normalized mutual information and greatly improved the tracking speed and accuracy by employing 2D/2D matching frameworks to obtain 3D camera motion (Helferty et al, 2007). Deligianni et al (2006) proposed bronchoscope tracking using a position sensor and a pq-based registration technique and improved its accuracy and stability by modeling respiratory motion with an active shape model. Their method generates VB images that most resemble RB images by recovering a bidirectional reflectance distribution function (BRDF) from registered RB and VB images .…”

Section: Introductionmentioning

confidence: 99%

Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Luo

Feuerstein

Deguchi

et al. 2012

Medical Image Analysis

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This paper presents a new hybrid camera motion tracking method for bronchoscopic navigation combining SIFT, epipolar geometry analysis, Kalman filtering, and image registration. In a thorough evaluation, we compare it to state-of-the-art tracking methods. Our hybrid algorithm for predicting bronchoscope motion uses SIFT features and epipolar constraints to obtain an estimate for interframe pose displacements and Kalman filtering to find an estimate for the magnitude of the motion. We then execute bronchoscope tracking by performing image registration initialized by these estimates. This procedure registers the actual bronchoscopic video and the virtual camera images generated from 3D chest CT data taken prior to bronchoscopic examination for continuous bronchoscopic navigation. A comparative assessment of our new method and the state-of-the-art methods is performed on actual patient data and phantom data. Experimental results from both datasets demonstrate a significant performance boost of navigation using our new method. Our hybrid method is a promising means for bronchoscope tracking, and outperforms other methods based solely on Kalman filtering or image features and image registration.

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Section: Introductionmentioning

confidence: 99%

Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Luo

Feuerstein

Deguchi

et al. 2012

Medical Image Analysis

View full text Add to dashboard Cite

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“…Mori et al [79] extended their navigation system by including a predictive Kalman filter [80]. In Deligianni et al [19], a more sophisticated approach involves shape knowledge of the bronchial tree.…”

Section: Organ Shift and Tissue Deformationmentioning

confidence: 99%

“…Promising solutions have already been introduced for lung bronchoscopy with the help of additional knowledge of the bronchial tree (e.g. by Mori et al [80], or Deligianni et al [19]). Also in navigated liver resection and laparoscopic RFA, organ motion is considered by means of laparoscopic ultrasound, as proposed e.g.…”

Section: Navigationmentioning

confidence: 99%

Navigation in Endoscopic Soft Tissue Surgery: Perspectives and Limitations

Baumhauer

Feuerstein

Meinzer

et al. 2008

Journal of Endourology

163

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Abstract-Despite rapid developments in the research areas medical imaging, medical image processing and robotics, the use of computer assistance in surgical routine is still limited to diagnostics, surgical planning, and interventions on mostly rigid structures. In order to establish a computer aided workflow from diagnosis to surgical treatment and follow-up, several proposals for computer assisted soft tissue interventions have been made in recent years. By means of different pre-and intraoperative information sources, like surgical plannings, intraoperative imaging, and tracking devices, surgical navigation systems aim at supporting surgeons in localizing anatomical targets, observing critical structures, and sparing healthy tissue. Current research in particular addresses the problem of organ shift and tissue deformation, and obstacles in communication between navigation system and surgeon.In this article, we review computer assisted navigation systems for soft tissue surgery. We concentrate on approaches which can be applied in endoscopic thoracic and abdominal surgery, as endoscopic surgery has special needs for image guidance due to limitations in perception. Furthermore, this article provides the reader with new trends and technologies in the area of computer assisted surgery. Finally, a balancing of key challenges and possible benefits of endoscopic navigation refines the perspectives of this increasingly important discipline of computer aided medical procedures.

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“…A pq-space based 2D/3D registration approach for bronchoscopic tracking was proposed by Deligianni et al [6,7]. They also incorporated shape modelling of airway deformation for non-rigid 2D/3D registration [8]. The surface normal for each pixel of video images were extracted using the algorithm derived by Rashid et al [21].…”

Section: Introductionmentioning

confidence: 99%

Robust camera localisation with depth reconstruction for bronchoscopic navigation

2015

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Purpose Bronchoscopy is a standard technique for airway examination, providing a minimally invasive approach for both diagnosis and treatment of pulmonary diseases. To target lesions identified pre-operatively, it is necessary to register the location of the bronchoscope to the CT bronchial model during the examination. Existing vision-based techniques rely on the registration between virtually rendered endobronchial images and videos based on image intensity or surface geometry. However, intensity-based approaches are sensitive to illumination artefacts while gradient-based approaches are vulnerable to surface texture.Methods In this paper, depth information is employed in a novel way to achieve continuous and robust camera localisation. Surface shading has been used to recover depth from endobronchial images. The pose of the bronchoscopic camera is estimated by maximising the similarity between the depth recovered from a video image and that captured from a virtual camera projection of the CT model. The normalised cross-correlation and mutual information have both been used and compared for the similarity measure.Results The proposed depth-based tracking approach has been validated on both phantom and in vivo data. It outperforms the existing vision-based registration methods resulting in smaller pose estimation error of the bronchoscopic camera. It is shown that the proposed approach is more robust to illumination artefacts and surface texture and less sensitive to camera pose initialisation.

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Nonrigid 2-D/3-D Registration for Patient Specific Bronchoscopy Simulation With Statistical Shape Modeling: Phantom Validation

Cited by 37 publications

References 28 publications

Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Development and comparison of new hybrid motion tracking for bronchoscopic navigation

Navigation in Endoscopic Soft Tissue Surgery: Perspectives and Limitations

Robust camera localisation with depth reconstruction for bronchoscopic navigation

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