ased Detection of Intrathoracic Airway Trees (1996)

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. We describe a novel rule-based method for the seg- mentation of airway trees from three-dimensional (3-D) sets of computed tomography (CT) images, and its validation. The pre- sented method takes advantage of a priori anatomical knowledge about pulmonary airway and vascular trees and their inter- relationships. 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. diseases, and in the assessment of the effects of time and ther- apy on the disease process. Since comparison of small changes in intrathoracic airways resulting over time from treatment can only be reliable if a quantitative scoring system is developed, automated or semiautomated detection of airway trees from HRCT image data is of paramount importance. Furthermore, a method developed to track airway structure over multiple lung volumes and across pharmacologic interventions will allow for advances in understanding of a broad-based set of physiologic and patho-physiologic conditions. Such airway tracking will not only allow for the assessment of the airway tree itself, but the tree structure also serves as a fiducial marker for the tracking of regional lung parenchyma. Identification of airway trees from 3-D CT image data sets is a very difficult problem and development of methods for airway tree detection suffers from several natural limitations of the image data quality inherent to CT scanning geometry and physics. Currently, HRCT scanning via the electron beam CT, as was used in this study, offers the best in-slice resolution of 0.4 /pixel. However, if the airway is not perpendicular to the scanning plane, its detectability may be lower due to partial volume effect. With a much lower image resolution along the z axis resulting from the lowest available slice thickness of 1.5 mm (we use 3 mm in the work presented here), many small objects like pulmonary airways or vessels become undetectable on a CT image. The situation is even worse in reality. Due to the fixed radiation exposure, the 1.5-mm-thick CT image slices exhibit much lower signal-to-noise ratio compared to the 3- mm-thick slices. As a result, 3-mm slice thickness is common in volumetric CT image data sets using the fastest electron beam CT scanning (scan aperture = 100 ms). The second limitation is linked to the in vivo character of CT imaging. While airway tree detection in isolated lung specimens may be achieved by simple threshold-based 3-D region growing, in vivo image data quality can be substantially influenced by breathing and heart motion artifacts, which make image analysis of such CT data sets difficult if not impossible. To fa- cilitate computerized analysis of thoracic CT data sets in vivo, complicated CT acquisition protocols must be followed-CT image acquisition must be electrocardiogram (ECG)-gated to minimize heart motion artifacts and can be volume controlled to minimize breathing motion artifacts (4). With such care, airways as small as or smaller than 1 mm can be visualized (but not quantified) and airway dimensions can be assessed for airways as small as 2 mm. In this paper, we study dogs in the order of 1%-kg body weight which is equivalent to a very young child. Thus, the analyzed dog airways are quite small and represent the worst case relative to humans. We describe an automated method for segmentation of airway trees from 3-D sets of CT images. The method is