Bronchoscopy is often performed for diagnosing lung cancer. The recent development of multidetector CT (MDCT) scanners and ultrathin bronchoscopes now enable the bronchoscopic biopsy and treatment of peripheral regions of interest (ROIs). Because the peripheral ROIs are often located several generations within the airway tree, careful planning is required prior to a procedure. The current practice for planning peripheral bronchoscopic procedures, however, is difficult, error-prone, and time-consuming. We propose a system for planning peripheral bronchoscopic procedures using patient-specific MDCT chest scans. The planning process begins with a semi-automatic segmentation of ROIs. The remaining system components are completely automatic, beginning with a new strategy for tracheobronchial airway-tree segmentation. The system then uses a new locally-adaptive approach for finding the interior airway-wall surfaces. From the polygonal airway-tree surfaces, a centerline-analysis method extracts the central axes of the airway tree. The system's route-planning component then analyzes the data generated in the previous stages to determine an appropriate path through the airway tree to the ROI. Finally, an automated report generator gives quantitative data about the route and both static and dynamic previews of the procedure. These previews consist of virtual bronchoscopic endoluminal renderings at bifurcations encountered along the route and renderings of the airway tree and ROI at the suggested biopsy location. The system is currently in use for a human lung-cancer patient pilot study involving the planning and subsequent live image-based guidance of suspect peripheral cancer nodules.
Robust and accurate segmentation of the human airway tree from multi-detector computed-tomography (MDCT) chest scans is vital for many pulmonary-imaging applications. As modern MDCT scanners can detect hundreds of airway tree branches, manual segmentation and semi-automatic segmentation requiring significant user intervention are impractical for producing a full global segmentation. Fully-automated methods, however, may fail to extract small peripheral airways. We propose an automatic algorithm that searches the entire lung volume for airway branches and poses segmentation as a global graph-theoretic optimization problem. The algorithm has shown strong performance on 23 human MDCT chest scans acquired by a variety of scanners and reconstruction kernels. Visual comparisons with adaptive region-growing results and quantitative comparisons with manually-defined trees indicate a high sensitivity to peripheral airways and a low false-positive rate. In addition, we propose a suite of interactive segmentation tools for cleaning and extending critical areas of the automatically segmented result. These interactive tools have potential application for image-based guidance of bronchoscopy to the periphery, where small, terminal branches can be important visual landmarks. Together, the automatic segmentation algorithm and interactive tool suite comprise a robust system for human airway-tree segmentation.
Modern MDCT and micro-CT scanners are able to produce high-resolution three-dimensional (3D) images of anatomical trees, such as the airway tree and the heart and liver vasculature. An important problem arising in many contexts is the matching of trees depicted in two different images. Three basic steps are used in order to match two trees: (1) image segmentation, to extract the raw trees from a given pair of 3D images; (2) axial-analysis, to define the underlying centerline structure of the trees; and (3) tree matching, to match the centerline structures of the trees. We focus on step (3). This task is complicated by several problems associated with current segmentation and axial-analysis methods, including missing branches, false branches, and other topological errors in the extracted trees. We propose a model-based approach in which the extracted trees are assumed to arise from an initially unknown common structure corrupted by a sequence of modelled topological deformations. We employ a novel mathematical framework to directly incorporate this model into the matching problem. Under this framework, it is possible to define the set of matches that are consistent with a given deformation model. The optimal match is the member of this set that maximizes a user-definable similarity measure. We present several such similarity measures based upon geometrical attributes (e.g., branch lengths, branching angles, and relative branchpoint locations as measured from the 3D image data). We locate the globally optimal match via an efficient dynamic programming algorithm. Our primary analytical result is a set of sufficient conditions on the user-definable similarity measure such that our dynamic programming algorithm is guaranteed to locate an optimal match. Experimental results have been generated for 3D human CT chest scans and micro-CT coronary arterial-tree images of mice. The resulting matches are in good agreement with correspondences defined by human experts.