Hybrid segmentation and exploration of the human lungs

Bartz, Dirk; Mayer, Dirk; Fischer, Jan; Ley, Sebastian; Río, Ángel del; Thust, Steffi; Heußel, Claus Peter; Kauczor, HU; Straßer, Wolfgang

doi:10.1109/visual.2003.1250370

Cited by 43 publications

(40 citation statements)

References 16 publications

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“…2.D). Also, unlike many previous approaches, [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] the developed method does not perform any path tracing procedure. In addition, this scheme is general in methodology and could be used to identify vascular structures.…”

Section: Discussionmentioning

confidence: 99%

“…However, despite its simplicity and efficiency, a purely region growing based operation frequently leads to leakage into the lung parenchyma (i.e., a sudden explosion) because of partial volume effects and/or image noise (artifacts), in particular, in the presence of diseases (e.g., emphysema). To alleviate the leakage issue and meanwhile identify more airways, a number of solutions have been developed, including (1) morphological based methods, [12][13][14][15] (2) knowledge or rule based methods, 7,[16][17][18] (3) template matching based methods, [19][20][21][22][23] (4) machine learning classifiers based methods, [24][25][26] and (5) shape analysis based methods. [27][28][29][30][31][32] A relatively detailed description of these approaches can be found in a review article.…”

Section: Introductionmentioning

confidence: 99%

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A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

Jin

et al. 2015

Med. Phys.

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Purpose: A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. Methods: Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed "loop" shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. Results: For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset. Conclusions:The authors' quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways. C 2015 American Association of Physicists in Medicine. [http://dx

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

Jin

et al. 2015

Med. Phys.

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“…2. Segmentation of the Trachea and Bronchi: The trachea and the main parts of the bronchial tree are segmented using 3D region growing with the fixed threshold range of −1,024 to −950 HU within the lung region, and are regarded as the true airway region [18]. A new seed point for the airways is determined by searching for a possible point within the tracheal region in the topmost superior slice of the chest volumetric CT scan.…”

Section: Classification Of Lung and Non-lung Regionsmentioning

confidence: 99%

Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

et al. 2014

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This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surfacefitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.

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“…Volumetric navigation. 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].…”

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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Hybrid segmentation and exploration of the human lungs

Cited by 43 publications

References 16 publications

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

Context-aware volume navigation

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