Shuping Qing

Tracing Based Segmentation for the Labeling of Individual Rib Structures in Chest CT Volume Data

We propose a fast and robust segmentation algorithm for the extraction and labeling of individual rib structures in chest CT volume data. A diagnostic system based on this algorithm can display 3D rib centerlines, contours and surfaces. A click on a rib point in any slice image will have the system identify instantly the individual rib it belongs to. The algorithm is based on a recursive tracing approach. The geometrical properties of the rib structure are explored to set up valid assumptions and model. At each step, statistical analysis is combined with dynamic programming to estimate the outer surface contour from the detected edges. The algorithm works reliably on CT volume data of variant doses and resolutions. This algorithm can be extended to other modalities. The detected centerlines can also be used for reliable and fast registration of in or cross-modality volume data of chest scans.

A novel visualization method for the ribs within chest volume data

The ribs within computed tomography (CT) images form curved structures intersecting the axial plane at oblique angles. Rib metastases and other pathologies of the rib are apparent in CT images. Analysis of the ribs using conventional 2D axial slice viewing involves manually tracking them through multiple slices. 3D visualization of the ribs also has drawbacks due to occlusion. Examination of a single rib may require repositioning the viewpoint several times in order to avoid other ribs. We propose a novel visualization method that eliminates rib curvatures by straightening each rib along its centerline. This reduces both 2D and 3D viewing complexities. Our method is based upon first segmenting and extracting the centerlines of each rib. These steps are done through a tracing based segmentation. Next, the centerlines are refined to a smoother contour. Each centerline is then used to resample and digitally straighten each rib. The result is a simplified volume containing only the straightened ribs, which can be quickly examined both in 3D and by scrolling through a series of about 40 slices. Additionally, a projection of the image can yield a single 2D image for examination. The method was tested on chest CT images obtained from patients both positive and negative for rib metastases. Running time was less than 15 seconds per dataset. Preliminary results demonstrate the effectiveness of the visualization in detecting and delineating these metastases.

Pulmonary lobe segmentation using the thin plate spline (TPS) with the help of the fissure localization areas

Lung lobe segmentation is clinically important for disease classification, treatment and follow-up of pulmonary diseases. Diseases such as tuberculosis and silicolis typically present in specific lobes i.e. almost exclusively the upper ones. However, the fissures separating different lobes are often difficult to detect because of their variable shape, appearance and low contrast in computed tomography images. In addition, a substantial fraction of patients have missing or incomplete fissures. To solve this problem, several methods have been employed to interpolate incomplete or missed fissures. For example, Pu et al. used an implicit surface fitting with different radial basis functions; Ukil et al. apply fast marching methods; and Ross et al. used an interactive thin plate spline (TPS) interpolation where the user selects the points that will be used to compute the fissure interpolation via TPS. In our study, results of an automated fissure detection method based on a plate-filter as well points derived from vessels were fed into an a robust TPS interpolation that ultimately defined the lobes. To improve the selection of detected points, we statistically determined the areas where fissures are localized from 19 data-sets. These areas were also used to constrain TPS fitting so it reflected the expected shape and orientation of the fissures, hence improving result accuracy. Regions where the detection step provided low response were replaced by points derived from a distance-to-vessels map. The error, defined as the Euclidian mean distance between ground truth points and the TPS fitted fissures, was computed for each dataset to validate our results. Ground truth points were defined for both exact fissure locations and approximate fissure locations (when the fissures were not clearly visible). The mean error was 5.64±4.83 mm for the exact ground truth points, and 10.01 ± 8.23 mm for the approximate ground truth points.