Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy

Schlathoelter, Thorsten; Lorenz, Cristian; Carlsen, Ingwer C.; Renisch, Steffen; Deschamps, Thomas

doi:10.1117/12.467061

Cited by 72 publications

(53 citation statements)

References 8 publications

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“…Most navigation techniques for volumetric data are specialized for navigation in tubular structures to support various virtual endoscopy applications: Virtual angioscopy [9], virtual colonoscopy [11], virtual sinus endoscopy [16] and virtual bronchoscopy [2]. Most of these techniques attach the virtual endoscope (i.e., the camera) to a precomputed or manually determined centerline [21]. Hence these techniques cannot provide an ad-hoc general-purpose navigation for arbitrary volumetric data.…”

Section: Related Workmentioning

confidence: 99%

Context-aware volume navigation

Diepenbrock

Ropinski

Hinrichs

2011

2011 IEEE Pacific Visualization Symposium

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Figure 1: Three subsequent screenshots as made during the usage of our image-based volume navigation metaphor. Without changing the navigation mode, the user is able to inspect the data set in a behavior similar to a trackball and can also fly through internal structures when a collision detection is present. To support the spatial-awareness, appropriate thumbnails are displayed. ABSTRACTThe trackball metaphor is exploited in many applications where volumetric data needs to be explored. Although it provides an intuitive way to inspect the overall structure of objects of interest, an in-detail inspection can be tedious -or when cavities occur even impossible. Therefore we propose a context-aware navigation technique for the exploration of volumetric data. While navigation techniques for polygonal data require information about the rendered geometry, this strategy is not sufficient in the area of volume rendering. Since rendering parameters, e.g., the transfer function, have a strong influence on the visualized structures, they also affect the features to be explored. To compensate for this effect we propose a novel image-based navigation approach for volumetric data. While being intuitive to use, the proposed technique allows the user to perform complex navigation tasks, in particular to get an overview as well as to perform an in-detail inspection without any navigation mode switches. The technique can be easily integrated into raycasting based volume renderers, needs no extra data structures and is independent of the data set as well as the rendering parameters. We will discuss the underlying concepts, explain how to enable the navigation at interactive frame rates using OpenCL, and evaluate its usability as well as its performance.

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Section: Related Workmentioning

confidence: 99%

Context-aware volume navigation

Diepenbrock

Ropinski

Hinrichs

2011

2011 IEEE Pacific Visualization Symposium

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“…The bronchial tree was segmented from the other anatomy using region growing [16] and morphological operators. Subsequently, the airway surface was reconstructed as a polygonal mesh by using the marching cubes algorithm.…”

Section: Image Based Modelling Of Brdfmentioning

confidence: 99%

Enhancement of Visual Realism with BRDF for Patient Specific Bronchoscopy Simulation

Chung

Deligianni

Shah

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. This paper presents a novel method for photorealistic rendering of the bronchial lumen by directly deriving matched shading and texture parameters from video bronchoscope images. 2D/3D registration is used to match video bronchoscope images with 3D CT scan of the same patient, such that patient specific modelling and simulation with improved visual realism can be achieved. With the proposed method, shading parameters are recovered by modelling the bidirectional reflectance distribution function (BRDF) of the visible surfaces by exploiting the restricted lighting configurations imposed by the bronchoscope. The derived BRDF is then used to predict the expected shading intensity such that a texture map independent of lighting conditions can be extracted. This allows the generation of new views not captured in the original bronchoscopy video, thus allowing free navigation of the acquired 3D model with enhanced photo-realism. BackgroundWith the maturity of minimal access surgery in recent years, there has been an increasing demand of patient specific simulation devices for both training and skills assessment. This is due to the fact that the complexity of the instrument controls, restricted vision and mobility, difficult hand-eye co-ordination, and the lack of tactile perception are major obstacles in performing minimal access surgeries. They require a high degree of manual dexterity from the operator. Computer simulation provides an attractive means of performing certain aspects of this training, particularly the hand eye co-ordination and instrument control. One significant challenge to computer based simulation is the creation of patient specific models combined with photo-realistic rendering so that basic as well as advanced surgical skills can be assessed with these simulation platforms.For patient specific bronchoscope simulation, a number of techniques have been proposed for co-registering bronchoscope videos with 3D tomographic data such that camera pose in relation to the bronchial tree during video bronchoscope examination can be derived [1,2] With the use of image based modelling and rendering techniques, it is possible to extend conventional texture mapping to support the representation of 3D surface details and view motion parallax in addition to photorealism [3,4,5]. One of the major challenges of combining 2D

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“…Previously proposed airway segmentation methods have employed four strategies: (a) knowledge-based techniques (21,23,37), (b) region growing (4,24,25,30,38), (c) central-axis analysis (20,27), and (d) mathematical morphology (22,26,28,29). The technique proposed by Sonka et al (21,23) uses an anatomic knowledge base describing structural relationships between airways and neighboring pulmonary vessels.…”

mentioning

confidence: 99%

“…Region-growing-based methods use voxel connectivity and a threshold to identify regions, usually through 26-connectivity (4,24,25,30,38). Summers et al (4) use 3D seeded region growing and a manually selected threshold to segment airways for rendering.…”

mentioning

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Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

et al. 2002

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Rationale and Objectives. The segmentation of airways from CT images is a critical first step for numerous virtual bronchoscopic (VB) applications. Automatic or semiautomatic methods are necessary, since manual segmentation is prohibitively time consuming. The methods must be robust and operate within a reasonable time frame to be useful for clinical VB use. The authors developed an integrated airway segmentation system and demonstrated its effectiveness on a series of human images. Materials and Methods.The authors' airway segmentation system draws on two segmentation algorithms: (a) an adaptive region-growing algorithm and (b) a new hybrid algorithm that uses both region growing and mathematical morphology. Images from an ongoing VB study were segmented by means of both the adaptive region-growing and the new hybrid methods. The segmentation volume, branch number estimate, and segmentation quality were determined for each case.Results. The results demonstrate the need for an integrated segmentation system, since no single method is superior for all clinically relevant cases. The region-growing algorithm is the fastest and provides acceptable segmentations for most VB applications, but the hybrid method provides superior airway edge localization, making it better suited for quantitative applications. In addition, the authors show that prefiltering the image data before airway segmentation increases the robustness of both regiongrowing and hybrid methods. Conclusion.The combination of these two algorithms with the prefiltering options allowed the successful segmentation of all test images. The times required for all segmentations were acceptable, and the results were suitable for the authors' VB application needs.Key Words. Bronchi, CT; bronchoscopy; computed tomography (CT), image processing; computed tomography (CT), threedimensional; trachea, CT. © AUR, 2002New multidetector spiral CT scanners can produce threedimensional (3D) volumetric images of the human airway tree consisting of hundreds of two-dimensional (2D) sections (1,2). A typical 3D image can include 400 or more 512 ϫ 512 0.6-mm sections. Such images provide an excellent basis for virtual bronchoscopy (VB) applications (3-15) and quantitative airway analysis (8,16 -20). New VB methods also allow for live guided nodule and lymph node biopsies (13,15). A critical first step in these VB applications is the segmentation of the airway tree. Manual interactive segmentation has been applied in some cases, but routine manual analysis is impractical for the large 3D images arising from the new scanners (21,22). A variety of semiautomatic airway segmentation techniques have been proposed, but none have been conclusively proved adequate for very large, high-resolution, 3D CT chest images (4,20 -30).

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Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy

Cited by 72 publications

References 8 publications

Context-aware volume navigation

Context-aware volume navigation

Enhancement of Visual Realism with BRDF for Patient Specific Bronchoscopy Simulation

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

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