Abstract: Generating a visualization of volumetric data can include receiving a set of three-dimensional CT data representative of a lung and receiving a selection of a lung segment corresponding to the subset of the CT data. Generating the visualization can include generating a multi-planar reformat (MPR) view representative of a portion of the lung intersecting a plane and generating a display image of the MPR view visually distinguishing a planar portion of the selected lung segment from a portion of the lung coplanar with the planar portion and not corresponding to the selected lung segment. Generating the visualization can include generating a topographic multi-planar reformat (tMPR) image and receiving a selection of a location within the tMPR image corresponding to a selected location in the selected lung subset. The MPR view can include the selected location in the selected lung segment.

Abstract: Techniques for generating a hollow model from a medical image are disclosed herein. In an example, a hollow model may be created within a medical imaging visualization application through the generation of a mask of an interior space of a segmented anatomical structure, the extrusion of a shell mask from the mask of the interior space, and the generation of a visualization of the shell mask within the medical imaging visualization application. For example, the mask may be provided as a layer in the medical imaging visualization application, allowing a user to visualize the produced shell mask of the hollow model from the perspective of the medical imaging. In further examples, the hollow model generation techniques may be used with techniques for shell region modifications, variable shell thickness, multiple shell layer, and trimming of shell endpoints.

Abstract: Techniques for generating a hollow model from a medical image are disclosed herein. In an example, a hollow model may be created within a medical imaging visualization application through the generation of a mask of an interior space of a segmented anatomical structure, the extrusion of a shell mask from the mask of the interior space, and the generation of a visualization of the shell mask within the medical imaging visualization application. For example, the mask may be provided as a layer in the medical imaging visualization application, allowing a user to visualize the produced shell mask of the hollow model from the perspective of the medical imaging. In further examples, the hollow model generation techniques may be used with techniques for shell region modifications, variable shell thickness, multiple shell layer, and trimming of shell endpoints.

Abstract: A method including searching image data corresponding to a series of axial image slices with a processor, searching axial image slices from a starting image slice and calculating a confidence score that an image slice includes a cross-section image of an aorta, identifying an image slice containing at least one seed disk, including an ascending aorta seed disk, from candidate image slices identified according to the confidence score, and growing a 3D segmentation of the ascending aorta by stacking ascending aorta image disks included in consecutive image slices beginning from the ascending aorta seed disk.

Abstract: A method of operating on volumetric imaging data representing organ anatomy includes determining whether a polyp location within a specified range from a viewing point is visible, or hidden by an anatomical feature, and marking the polyp as visible or hidden with a viewable indicator.

Abstract: A method including searching image data corresponding to a series of axial image slices with a processor, searching axial image slices from a starting image slice and calculating a confidence score that an image slice includes a cross-section image of an aorta, identifying an image slice containing at least one seed disk, including an ascending aorta seed disk, from candidate image slices identified according to the confidence score, and growing a 3D segmentation of the ascending aorta by stacking ascending aorta image disks included in consecutive image slices beginning from the ascending aorta seed disk.

Abstract: A method including searching image data corresponding to a series of axial image slices with a processor, searching axial image slices from a starting image slice and calculating a confidence score that an image slice includes a cross-section image of an aorta, identifying an image slice containing at least one seed disk, including an ascending aorta seed disk, from candidate image slices identified according to the confidence score, and growing a 3D segmentation of the ascending aorta by stacking ascending aorta image disks included in consecutive image slices beginning from the ascending aorta seed disk.

Abstract: This document discusses, among other things, systems and methods for efficiently calculating a colon segmentation from one or more candidate virtual three-dimensional objects. A sequence of image scans are analyzed and candidate segments are identified. Landmark segments are identified from the candidate segments. A characteristic path is generated for each candidate segment. The paths are joined using a cost network and reoriented to be consistent with a typical flythrough path. The connected path is then used to generate a continuous volumetric virtual object.

Abstract: This document discusses, among other things, systems and methods for efficiently calculating a colon segmentation from one or more candidate virtual three-dimensional objects. A sequence of image scans are analyzed and candidate segments are identified. Landmark segments are identified from the candidate segments. A characteristic path is generated for each candidate segment. The paths are joined using a cost network and re-oriented to be consistent with a typical flythrough path. The connected path is then used to generate a continuous volumetric virtual object.

Abstract: This document discusses, among other things, systems and methods for efficiently calculating a registration of multiple characteristic paths of a virtual three-dimensional object. Each path of discrete points is transformed into a piecewise linear parameterization as a function of path length. The paths are smoothed and normalized. The shorter path is partitioned into a number of discrete subintervals. The subintervals are mapped to the longer path using a minimization function that minimizes a cost function resulting in a locally optimal registration. The shorter path is incrementally positioned along the longer path and the minimization is attempted at each position. When the shorter path cannot be shifted any farther, the globally optimal registration is returned.

Abstract: This document discusses, among other things, systems and methods for efficiently calculating a colon segmentation from one or more candidate virtual three-dimensional objects. A sequence of image scans are analyzed and regions that represent air-filled objects and tagged-stool are identified as candidate segments. A characteristic path is generated for each candidate segment. The paths are joined using a cost network and re-oriented to be consistent with a typical flythrough path. The connected path is then used to generate a continuous volumetric virtual object.