Abstract: Method and processes for segmentation of lungs lobes from CT image data are disclosed. Lobe segmentation relies on other segmentations, including the lungs, the lung airways and vasculature. Segmentation methods and processes begin with the acquisition of 3-D image data such as from a high resolution CT scan of a patient's lungs or at least a region thereof. The image is then processed to provide a segmentation mask from which the components are extracted to identify the lungs and airway. Lung vasculature and lobes are identified by similar processes.

Abstract: A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Abstract: Method and processes for segmentation of lungs lobes from CT image data are disclosed. Lobe segmentation relies on other segmentations, including the lungs, the lung airways and vasculature. Segmentation methods and processes begin with the acquisition of 3-D image data such as from a high resolution CT scan of a patient's lungs or at least a region thereof. The image is then processed to provide a segmentation mask from which the components are extracted to identify the lungs and airway. Lung vasculature and lobes are identified by similar processes.

Abstract: A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Abstract: A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Abstract: A method of extending a segmentation of an image using navigated image data from a navigation system includes tracking, with the navigation system, at least one of a traveled path and a position of an imaging device relative to an initial segmentation of 3D image data including an initial treelike structure. Navigated image data including image data including at least one 2D or 3D image is captured with the imaging device. A point from the navigated image data corresponding to a potential airway structure is obtained by the navigation system. The initial segmentation of 3D image data is extended by the navigation system using the point obtained from the navigated image data.

Abstract: A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.

Abstract: A method of extending a segmentation of an image using navigated image data from a navigation system includes tracking, with the navigation system, at least one of a traveled path and a position of an imaging device relative to an initial segmentation of 3D image data including an initial treelike structure. Navigated image data including image data including at least one 2D or 3D image is captured with the imaging device. A point from the navigated image data corresponding to a potential airway structure is obtained by the navigation system. The initial segmentation of 3D image data is extended by the navigation system using the point obtained from the navigated image data.

Abstract: A computer implemented method for cognitive testing, specifically for dementia-related afflictions. The method is administered on a computer workstation in an interactive, web-based format and screens the cognitive abilities of the patient. The method includes evaluation of five areas of cognitive domain including visuospatial skills and executive function by means of a clock face test, memory, verbal fluency, attention, and orientation, all of which are essential for indications of dementia, including Alzheimer's disease and other cognitive afflictions. The method does not require administration by a trained professional, but rather, is administered in a web browser either from a server to the workstation via the Internet or locally at the workstation.