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.
Recent studies have found correlation between the risk of rupture of saccular aneurysms and their morphological
characteristics, such as volume, surface area, neck length, among others. For reliably exploiting these parameters
in endovascular treatment planning, it is crucial that they are accurately quantified. In this paper, we present
a novel framework to assist physicians in accurately assessing saccular aneurysms and efficiently planning for
endovascular intervention. The approach consists of automatically segmenting the pathological vessel, followed
by the construction of its surface representation. The aneurysm is then separated from the vessel surface
through a graph-cut based algorithm that is driven by local geometry as well as strong prior information. The
corresponding healthy vessel is subsequently reconstructed and measurements representing the patient-specific
geometric parameters of pathological vessel are computed. To better support clinical decisions on stenting and
device type selection, the placement of virtual stent is eventually carried out in conformity with the shape of the
diseased vessel using the patient-specific measurements. We have implemented the proposed methodology as a
fully functional system, and extensively tested it with phantom and real datasets.
A simple and efficient method for guiding 2D-image reading for colon screening is proposed. It provides visual
feedback by highlighting the region of interest in the current 2D cross section and indicates the direction in which to
scroll based on the anatomical structure of the colon given by the centerline. Unobserved areas are calculated using a
region growing algorithm and displayed in a 3D view to guarantee a complete inspection. This technique is intended to
significantly reduce any chance of inadvertently skipping over portions of the colon in the inspection process and to
generate faster examination times. The visual feedback can also be used as a guided learning tool for inexperienced
radiologists.
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