Segmentation of pulmonary blood vessels from three-dimensional (3D) multi-detector CT (MDCT) images is
important for pulmonary applications. This work presents a method for extracting the vascular trees of the
pulmonary arteries and veins, applicable to both contrast-enhanced and unenhanced 3D MDCT image data.
The method finds 2D elliptical cross-sections and evaluates agreement of these cross-sections in consecutive
slices to find likely cross-sections. It next employs morphological multiscale analysis to separate vessels from
adjoining airway walls. The method then tracks the center of the likely cross-sections to connect them to the
pulmonary vessels in the mediastinum and forms connected vascular trees spanning both lungs. A ground-truth
study indicates that the method was able to detect on the order of 98% of the vessel branches having diameter
≥ 3.0 mm. The extracted vascular trees can be utilized for the guidance of safe bronchoscopic biopsy.
Accurate definition of the aorta and pulmonary artery from three-dimensional (3D) multi-detector CT (MDCT)
images is important for pulmonary applications. This work presents robust methods for defining the aorta and
pulmonary artery in the central chest. The methods work on both contrast enhanced and no-contrast 3D MDCT
image data. The automatic methods use a common approach employing model fitting and selection and adaptive
refinement. During the occasional event that more precise vascular extraction is desired or the method fails, we
also have an alternate semi-automatic fail-safe method. The semi-automatic method extracts the vasculature
by extending the medial axes into a user-guided direction. A ground-truth study over a series of 40 human 3D
MDCT images demonstrates the efficacy, accuracy, robustness, and efficiency of the methods.
Accurate definition of the central-chest vasculature from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. For instance, the aorta and pulmonary artery help in automatic definition of the Mountain lymph-node stations for lung-cancer staging. This work presents a system for defining
major vascular structures in the central chest. The system provides automatic methods for extracting the aorta and pulmonary artery and semi-automatic methods for extracting the other major central chest arteries/veins, such as the superior vena cava and azygos vein. Automatic aorta and pulmonary artery extraction are performed
by model fitting and selection. The system also extracts certain vascular structure information to validate outputs. A semi-automatic method extracts vasculature by finding the medial axes between provided important sites. Results of the system are applied to lymph-node station definition and guidance of bronchoscopic biopsy.
Bronchoscopic biopsy of the central-chest lymph nodes is vital in the staging of lung cancer. Three-dimensional
multi-detector CT (MDCT) images provide vivid anatomical detail for planning bronchoscopy. Unfortunately,
many lymph nodes are situated close to the aorta, and an inadvertent needle biopsy could puncture the aorta,
causing serious harm. As an eventual aid for more complete planning of lymph-node biopsy, it is important to
define the aorta. This paper proposes a method for extracting the aorta from a 3D MDCT chest image. The
method has two main phases: (1) Off-line Model Construction, which provides a set of training cases for fitting
new images, and (2) On-Line Aorta Construction, which is used for new incoming 3D MDCT images. Off-Line
Model Construction is done once using several representative human MDCT images and consists of the following
steps: construct a likelihood image, select control points of the medial axis of the aortic arch, and recompute
the control points to obtain a constant-interval medial-axis model. On-Line Aorta Construction consists of the
following operations: construct a likelihood image, perform global fitting of the precomputed models to the
current case's likelihood image to find the best fitting model, perform local fitting to adjust the medial axis
to local data variations, and employ a region recovery method to arrive at the complete constructed 3D aorta.
The region recovery method consists of two steps: model-based and region-growing steps. This region growing
method can recover regions outside the model coverage and non-circular tube structures. In our experiments,
we used three models and achieved satisfactory results on twelve of thirteen test cases.