<title>Knowledge-based segmentation of intrathoracic airways from multidimensional high-resolution CT images</title>

Abstract

A critically important component in the development of new methods for treatment of pulmonary diseases is the development of sensitive techniques for assessing alterations in regional lung structure and function. We describe an automated method for segmentation of airway trees from three-dimensional sets of CT images. The method is based on a combination of conventional three-dimensional seeded region growing that is used to identify large airways, knowledge-based two-dimensional segmentation of individual CT slices to identify probable locations of smaller diameter airways, and merging of airway regions across the three-dimensional set of slices resulting in a tree-like airway structure. The preliminary validation of the method was done in eighty 3 mm thick CT sections from two 40 slice data sets of a canine thorax scanned with lungs held at 1 .5 kPa and 2.5 kPa. The method's performance was compared with that of the conventional threedimensional region growing method. The knowledge-based approach to identification of potential airways in individual image slices substantially outperforms the conventional method and promises to be applicable to in vivo pulmonary CT images.

Optimal Graph Search Based Segmentation of Airway Tree Double Surfaces Across Bifurcations

“…and Sonka et al . focused on segmenting 3-D canine bronchial trees [10], [11]. Once electron-beam human lung CT images became available, methods for human airway tree detection quickly followed.…”

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

“…Previous work on airway segmentation mainly includes region growing-based methods [1], [2], [3], [4], morphology based methods [5], [6], [7], and combinations of the two [8], [9], [10]. Other methods proposed in the past include rule based methods [11], [12], energy function minimization [7], and ROI modification-based techniques [13]. Schlathölter et al [14] use a front-propagation algorithm for segmenting airway trees.…”

Innovative molecular and imaging approaches for the detection of lung cancer and its precursor lesions

“…Current efforts can visualize to about the sixth branch level or down to a diameter of 1.2 mm (Mori et al, 2000). Semi-automated and automated construction of a virtual map of the central airways can be readily done (Sonka et al, 1994;Summers et al, 1998). Newer high resolution spiral CT systems will enable even finer virtual mapping of the bronchus (Dawn et al, 2001;Haponik et al, 1999).…”