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

Mirota, Daniel; Wang, H.; Taylor, Russell H.; Ishii, Masaru; Gallia, Gary L.; Hager, Gregory D.

doi:10.1109/tmi.2011.2176500

Cited by 53 publications

(40 citation statements)

References 38 publications

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“…While several papers have used external tracking systems to enable real-time guidance during surgery (Konen et al [80] (2007), Winne et al [165] (2011), Schulze et al [124] (2010), Daly et al [45] (2010)), Shahidi et al [129] (2002) and Lapeer et al [85] (2008) used passive optical markers and image-processing techniques to substitute for the external tracking system. Mirota et al [102], [103] (2009-2012) argued that direct registration of a 3D reconstruction from endoscopic video and a preoperative CT scan can improve accuracy, since the detour through external tracking systems tends to introduce significant errors. They applied shape-from-motion algorithms to reconstruct a surface model from the monocular endoscopic video for CT registration.…”

Section: Approachesmentioning

confidence: 99%

“…Mourgues et al [111] created a surface model to visualize the heart, suppressing surgical instruments, based on stereo vision. The reconstruction of sinusoidal cavities for video/CT registration with SfM has been described by Mirota et al [102], [103], Wang et al [155], [156], Burschka et al [30], [31] and Wittenberg et al [166]. In laparoscopy, Bouma et al [26] have used SfM to reconstruct the abdominal cavity (using either monocular or stereoscopic images).…”

Section: E Projection Surfacementioning

confidence: 99%

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Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Bergen

Wittenberg

2016

IEEE J. Biomed. Health Inform.

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Endoscopic procedures form part of routine clinical practice for minimally invasive examinations and interventions. While they are beneficial for the patient, reducing surgical trauma and making convalescence times shorter, they make orientation and manipulation more challenging for the physician, due to the limited field of view through the endoscope. However, this drawback can be reduced by means of medical image processing and computer vision, using image stitching and surface reconstruction methods to expand the field of view. This paper provides a comprehensive overview of the current state of the art in endoscopic image stitching and surface reconstruction. The literature in the relevant fields of application and algorithmic approaches is surveyed. The technological maturity of the methods and current challenges and trends are analyzed.

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Section: Approachesmentioning

confidence: 99%

Section: E Projection Surfacementioning

confidence: 99%

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Bergen

Wittenberg

2016

IEEE J. Biomed. Health Inform.

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“…9 Recently, an image-based registration method achieved reprojection error 0.7 mm 10 but this methods require an initial registration to function. Our work is closely related to, 11 where a sparse 3D point cloud is computed from a sequence of endoscopic images and then registered to a 3D geometry derived from a CT scan.…”

Section: Introductionmentioning

confidence: 99%

Image-based navigation for functional endoscopic sinus surgery using structure from motion

et al. 2016

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Functional Endoscopic Sinus Surgery (FESS) is a challenging procedure for otolaryngologists and is the main surgical approach for treating chronic sinusitis, to remove nasal polyps and open up passageways. To reach the source of the problem and to ultimately remove it, the surgeons must often remove several layers of cartilage and tissues. Often, the cartilage occludes or is within a few millimeters of critical anatomical structures such as nerves, arteries and ducts. To make FESS safer, surgeons use navigation systems that register a patient to his/her CT scan and track the position of the tools inside the patient. Current navigation systems, however, suffer from tracking errors greater than 1 mm, which is large when compared to the scale of the sinus cavities, and errors of this magnitude prevent from accurately overlaying virtual structures on the endoscope images. In this paper, we present a method to facilitate this task by 1) registering endoscopic images to CT data and 2) overlaying areas of interests on endoscope images to improve the safety of the procedure. First, our system uses structure from motion (SfM) to generate a small cloud of 3D points from a short video sequence. Then, it uses iterative closest point (ICP) algorithm to register the points to a 3D mesh that represents a section of a patients sinuses. The scale of the point cloud is approximated by measuring the magnitude of the endoscope’s motion during the sequence. We have recorded several video sequences from five patients and, given a reasonable initial registration estimate, our results demonstrate an average registration error of 1.21 mm when the endoscope is viewing erectile tissues and an average registration error of 0.91 mm when the endoscope is viewing non-erectile tissues. Our implementation SfM + ICP can execute in less than 7 seconds and can use as few as 15 frames (0.5 second of video). Future work will involve clinical validation of our results and strengthening the robustness to initial guesses and erectile tissues.

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“…The extracted image features can be used for tissue tracking [1]- [3], deformation recovery [4], [5], 3D reconstruction [6], [7], endoscope localization [8]- [10], augmented reality [8], [10], [11], and intra-operative registration [3], [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient Vessel Feature Detection for Endoscopic Image Analysis

Lin

Sun

Sánchez

et al. 2015

IEEE Trans. Biomed. Eng.

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Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgeness-based circle test and ridgeness-based branching segment detection are presented to extract branching points and branching segments, respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points.More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public.

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

Cited by 53 publications

References 38 publications

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Stitching and Surface Reconstruction From Endoscopic Image Sequences: A Review of Applications and Methods

Image-based navigation for functional endoscopic sinus surgery using structure from motion

Efficient Vessel Feature Detection for Endoscopic Image Analysis

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