Rule-based detection of intrathoracic airway trees

Sonka, Milan; Park, Wonkyu; Hoffman, Eric A.

doi:10.1109/42.500140

Cited by 111 publications

(74 citation statements)

References 30 publications

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“…Some of the approaches are based on mathematical morphology [2], central axis analysis [3], region growing [4], and knowledge-based techniques [5]. There is a trend towards hybrid approaches using a combination of techniques to give better results in terms of speed and detail of the extraction process [6].…”

Section: Introductionmentioning

confidence: 99%

Automated 3D Segmentation of the Lung Airway Tree Using Gain-Based Region Growing Approach

Singh

Crawford

Curtin

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. In diagnosing lung diseases, it is highly desirable to be able to segment the lung into physiological structures, such as the intra-thoracic airway tree and the pulmonary structure. Providing an in-vivo and non-invasive tool for 3D reconstruction of anatomical tree structures such as the bronchial tree from 2D and 3D data acquisitions is a challenging issue for computer vision in medical imaging. Due to the complexity of the tracheobronchial tree, the segmentation task is non trivial. This paper describes a 3D adaptive region growing algorithm incorporating gain calculation for segmenting the primary airway tree using a stack of 2D CT slices. The algorithm uses an entropy-based measure known as information gain as a heuristic for selecting the voxels that are most likely to represent the airway regions.

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Section: Introductionmentioning

confidence: 99%

Automated 3D Segmentation of the Lung Airway Tree Using Gain-Based Region Growing Approach

Singh

Crawford

Curtin

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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“…Sonka et al 10 describe a rule-based method for the segmentation of airway trees from 3D sets of CT images. The method is based on a combination of 3D seeded region growing that is used to identify large airways, rule-based two-dimensional (2D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and finally a merging of airway regions across the 3D set of slices resulting in a tree-like airway structure.…”

Section: Prior Workmentioning

confidence: 99%

Automatic Detection of Bronchial Dilatation in HRCT Lung Images

2008

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Bronchiectasis is an airway disease caused by the dilatation of the bronchial tree, and a bronchovascular pair is formed between a bronchus and a vessel. An abnormal bronchovascular pair is one that has a larger bronchus compared to its accompanying vessel. Typically, bronchi and vessels running perpendicular to the plane of section appear as near-circular rings on computed tomography (CT) scans. This paper describes BV_pairs, a system capable of detecting abnormal bronchovascular pairs in high-resolution CT scans of sparse datasets using a three-stage process: (1) detection of potential bronchovascular pairs, (2) detection of discrete pairs, where there exists no ambiguity as to the artery that accompanies a bronchus, and (3) identification of abnormal pairs with severity levels. The system was evaluated at every stage. The automated scoring for the presence and severity of bronchial abnormalities was demonstrated to be comparable to that of an experienced radiologist (i.e., kappa statistics κ90.5). In addition, BV_pairs was also evaluated on images containing honeycombing regions, since honeycombing cysts appear very similar to bronchi, and the system could successfully differentiate honeycombing cysts from bronchi.

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“…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).…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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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Rule-based detection of intrathoracic airway trees

Cited by 111 publications

References 30 publications

Automated 3D Segmentation of the Lung Airway Tree Using Gain-Based Region Growing Approach

Automated 3D Segmentation of the Lung Airway Tree Using Gain-Based Region Growing Approach

Automatic Detection of Bronchial Dilatation in HRCT Lung Images

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

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