Combined endoscopic video tracking and virtual 3D CT registration for surgical guidance

Helferty, J.P.; Higgins, William E.

doi:10.1109/icip.2002.1040112

Cited by 20 publications

(28 citation statements)

References 11 publications

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“…66 Similar work based on optical flow has also been reported. 43 The main goal of such methods is to determine the motion of the camera during a procedure but this inherently involves inferring information about the structure of the scene at the same time. Currently, the most advanced systems for SfM in MAS make use of preoperative models of the anatomy to impose additional constraints.…”

Section: Moving Scope Techniquesmentioning

confidence: 99%

Surgical Vision

Stoyanov

2011

Ann Biomed Eng

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The emergence of Minimal Access Surgery (MAS) as a paradigm in modern healthcare treatment has created new challenges and opportunities for automated image understanding and computer vision. In MAS, images recovered from inside the body using specialized devices are used to visualize and operate on the surgical site but they can also be used to computationally infer in vivo 3D tissue surface shape, soft-tissue morphology, and surgical instrument motion. This information is important for facilitating in vivo biophotonic imaging modalities where the interaction between light and tissue is used to infer the structural and functional properties of the tissue. This article provides a review of the literature for computer vision and image understanding techniques applied to MAS images. The focus of this article is to elucidate a perspective on how computer vision techniques can be used to support and enhance the capabilities of biophotonic imaging modalities during surgery. Note that while MAS encompasses a variety of surgical specializations this review does not involve procedures performed in the interventional suite. The review has been carried out based on searches in the PubMed and IEEE databases using the article's keywords.

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Section: Moving Scope Techniquesmentioning

confidence: 99%

Surgical Vision

Stoyanov

2011

Ann Biomed Eng

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“…It is usually done from video stream using corresponding points and epipolar geometry [2] or optical flow methods and perspective geometry [4]. In turn, next position of the endoscope camera can be predicted and tracked with Kalman using [5] or Monte Carlo [6] particle filters.…”

Section: Introductionmentioning

confidence: 99%

Computational Tasks in Bronchoscope Navigation During Computer-Assisted Transbronchial Biopsy

Bułat

Duda

Socha

et al. 2008

Computational Science – ICCS 2008

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Abstract. The paper presents algorithmic solutions dedicated to computer navigation system which is to assist bronchoscope positioning during transbronchial needle-aspiration biopsy. The navigation exploits principle of on-line registration of real images coming from endoscope camera and virtual ones generated on the base of computed-tomography (CT) data of a patient. When these images are similar an assumption is made that the bronchoscope and virtual camera have approximately the same position and view direction. In the paper the following computational aspects are described: correction of camera lens distortion, fast approximate estimation of endoscope ego-motion, reconstruction of bronchial tree from CT data by means of their segmentation and its centerline calculation, virtual views generation, registration of real and virtual images via maximization of their mutual information and, finally, efficient parallel and network implementation of the navigation system which is under development.

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“…For example, endoscopic images can be combined with 3D data from Computed Tomography (CT), Magnetic Resonance (MR), or Laser Range Finder (LSR) ( [7,8,9,11]). Such methods are not only expensive but also difficult to use and thus have only seen limited application.…”

Section: Introductionmentioning

confidence: 99%