Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system

Abstract. Segmentation of lungs with large tumors is a challenging and time-consuming task, especially for 4D CT data sets used in radiation therapy. Existing lung segmentation methods are ineffective in these cases, because they are either not able to deal with large tumors and/or process every 3D image independently neglecting temporal information. In this paper, we present a approach for model-based 4D segmentation of lungs with large tumors in 4D CT data sets. In our approach, a 4D statistical shape model that accounts for inter-and intra-patient variability is fitted to the 4D image sequence, and the segmentation result is refined by a 4D graph-based optimal surface finding. The approach is evaluated using 10 4D CT data sets of lung tumor patients. The segmentation results are compared with a standard intensity-based approach and a 3D version of the presented model-based segmentation method. The intensity-based approach shows a better performance for normal lungs, however, fails in presence of large lung tumors. Although overall performance of 3D and 4D model-based segmentation is similar, the results indicate improved temporal coherence and improved robustness with respect to the segmentation parameters for the 4D model-based segmentation.

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“…Initial placing of mean shape S 4D (0, 0, ϕ). The initial ϕ is determined by a heuristic based on the detected bronchial tree [10].…”

Section: Fitting the Model To An Image Sequencementioning

confidence: 99%

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

Wilms

Ehrhardt

Handels

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…[9]. In Step 2), we emphasize the edge of air region using Laplacian Operator, and grow the initial air region obtained by Step 1) until it correspond to edge using region growing method morphologically.…”

Section: Bronchus and Blood Vesselsmentioning

confidence: 99%

Pulmonary Organs Analysis Method and Its Evaluation Based on Thoracic Thin-Section CT Images

Tanaka

Tozaki

Kawata

et al. 1999

Medical Image Computing and Computer-Assisted Intervention – MICCAI’99

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Abstract.To diagnose the lung cancer as to determine if it has malignant or benign nature, it is important to understand the spatial relationship among the abnormal nodule and other pulmonary organs. But the lung field has very complicated structure, so it is difficult to understand the connectivity of the pulmonary organs using thin-section CT images. This method consists of two parts. The first is the classification of the pulmonary structure based on the anatomical information. The second is the quantitative analysis that is then applicable to differential diagnosis, such as differentiation of malignant or benign abnormal tissue.

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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.…”

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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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Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system

Cited by 69 publications

References 3 publications

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

A 4D Statistical Shape Model for Automated Segmentation of Lungs with Large Tumors

Pulmonary Organs Analysis Method and Its Evaluation Based on Thoracic Thin-Section CT Images

Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy

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