Airway Tree Segmentation Research Articles

Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.

Segmentation and Quantitative Analysis of Intrathoracic Airway Trees from Computed Tomography Images

The segmentation of the human airway tree from volumetric multidetector-row computed tomography images is an important prerequisite for many clinical applications and physiologic studies. We present a new airway segmentation method based on fuzzy connectivity. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This method works on various types of scans (low dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-growing segmentation algorithm shows that this method retrieves a significantly higher count of airway branches. In an additional processing step, this method provides accurate cross-sectional airway measurements that are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations. The utility of the reported method is demonstrated in a comparative analysis of normal and cystic fibrosis airway trees.

Smoothing Lung Segmentation Surfaces in Three-dimensional X-ray CT Images Using Anatomic Guidance 1

Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies. Therefore there is a need for a more consistent lung surface near the mediastinum. This paper presents a fully automatic method for the three-dimensional smoothing of the lung boundary using information from the segmented human airway tree. First, using the segmented airway tree, we define a bounding box around the mediastinum for each lung, within which all operations are performed. We then define all generations of the airway tree distal to the right and left main stem bronchi to be part of the respective lungs and exclude all other segments. Finally, we perform a fast morphological closing with an ellipsoidal kernel to smooth the surface of the lung. This method has been tested by processing the segmented lungs from eight normal datasets. The mean value of the magnitude of curvedness of the lung contours, averaged over all the datasets, is 0.13 postsmoothing, compared with 4.91 before smoothing. The accuracy of the lung contours after smoothing is assessed by comparing the automatic results to manually traced smooth lung borders by a human analyst. Averaged over all volumes, the root mean square difference between human and computer borders is 0.8691 mm after smoothing, compared with 1.3012 mm before smoothing. The mean similarity index, which is an area overlap measure based on the kappa statistic, is 0.9958 (SD 0.0032), after smoothing. We have described a novel scheme for smoothing the lung contour around the mediastinum. The method is based on using anatomic information from the segmented airway tree. The validation results show that there is good agreement between manual and computer results. Because there are no accepted criteria for defining the lung boundary near the mediastinum, we believe our method of defining the boundary based on the structure of the airway tree provides a good basis for three-dimensional smoothing.

A subway map for the lungs: Planar occlusion-free visualization of the bronchial tree

High resolution CT datasets from multi-slice scanners allow the inspection of thetracheobronchial tree down to the smaller airways for diagnostic purposes (e.g. to inspectairway lumen and bronchial wall thickness). Using manual or automated segmentationmethods, the bronchial tree structure may be discernable down to hundreds of subsegmentsof higher generations. Linear projections (such as maximum intensity projections) of thedelicate three-dimensional tree lead to multiple occlusions (left figure), and make theinspection and navigation along specific airway paths cumbersome.The planar mapping is computed automatically for each automatically extractedindividual bronchial tree. It gives an overview of the bronchial tree in a single glance, isprintable (e.g. for the diagnostic report), allows to convey diagnostic CAD-information tothe reader through color-coding (e.g. anomalies in wall thickness or broncho-arterialdiameter ratios), and eases three-dimensional navigation through the bronchial tree (themap is bijectively linked to the 3D image volume).The suggested mapping is also applicable to show anomalies in the pulmonary arterialtree.Two possible approaches for automatic production of a non-intersecting topological mapping:multi-slice CT lung segmentation airway tree segmentation centerline extractionThe topological mapping (subway map) can aid the navigationthrough the bronchial tree. A mouse-click into the subway map willset the ortho-viewer to the corresponding true 3D-position, indicatethe position in the three-dimensional tree model, and show areformat of the selected airway location which is reformattedperpendicular to the local airway orientation .(a) At each bifurcation of generation i, the mappingbranches by an angle ± θ

Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions

Virtual bronchoscopy (VB) has emerged as a paradigm for more effective 3D CT image evaluation. Systematic evaluation of a 3D CT chest image using VB techniques, however, requires precomputed guidance data. This guidance data takes the form of central axes, or centerlines, through the major airways. We propose an axes-generation algorithm for VB assessment of 3D CT chest images. For a typical high-resolution 3D CT chest image, the algorithm produces a series of airway-tree axes, corresponding airway cross-sectional area measurements, and a segmented airway tree in a few minutes on a standard PC. Results for phantom and human airway-obstruction cases demonstrate the efficacy of the algorithm. Also, the algorithm is demonstrated in the context of VB-based 3D CT assessment.

Segmentation of intrathoracic airway trees: a fuzzy logic approach.

Three-dimensional (3-D) analysis of airway trees extracted from computed tomography (CT) image data can provide objective information about lung structure and function. However, manual analysis of 3-D lung CT images is tedious, time consuming and, thus, impractical for routine clinical care. We have previously reported an automated rule-based method for extraction of airway trees from 3-D CT images using a priori knowledge about airway-tree anatomy. Although the method's sensitivity was quite good, its specificity suffered from a large number of falsely detected airways. We present a new approach to airway-tree detection based on fuzzy logic that increases the method's specificity without compromising its sensitivity. The method was validated in 32 CT image slices randomly selected from five volumetric canine electron-beam CT data sets. The fuzzy-logic method significantly outperformed the previously reported rule-based method (p < 0.002).

Rule-based detection of intrathoracic airway trees

New sensitive and reliable methods for assessing alterations in regional lung structure and function are critically important for the investigation and treatment of pulmonary diseases. Accurate identification of the airway tree will provide an assessment of airway structure and will provide a means by which multiple volumetric images of the lung at the same lung volume over time can be used to assess regional parenchymal changes. The authors describe a novel rule-based method for the segmentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The presented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their interrelationships. The method is based on a combination of 3-D seeded region growing that is used to identify large airways, rule-based two-dimensional (2-D) segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the 3-D set of slices resulting in a tree-like airway structure. The method was validated in 40 3-mm-thick CT sections from five data sets of canine lungs scanned via electron beam CT in vivo with lung volume held at a constant pressure. The method's performance was compared with that of the conventional 3-D region growing method. The method substantially outperformed an existing conventional approach to airway tree detection.