Automated detection of airway and vessel orientations for quantitative analysis and visualization

A method, comprising:

• displaying a three-dimensional (3D) image of a lung, wherein the 3D image includes a segmented vessel tree;
• receiving a selection of a vessel of the lung, wherein the selected vessel is identified by a click-point outside the segmented vessel tree;
• locally segmenting the selected vessel, determining its orientation/direction and computing a two-dimensional (2D) cross-section of the vessel parallel to the vessel'long axis;
• and
• displaying a (2D) cross-section image of the vessel parallel to the vessel's long axis, wherein the display of the 2D cross-section image occurs immediately after the selection of the vessel is received.
• wherein the selected vessel is locally segmented by:
• creating a volume of interest (VOI) around a point of the 3D image, wherein the point identifies the selected vessel;
• performing a 3D segmentation of the vessel within the VOI, wherein the 3D segmentation of the vessel includes minimum and maximum thresholds based on a neighborhood of the click-point, wherein the minimum threshold is set to prevent the 3D segmentation from expanding into a bifurcation and maximum threshold is a Hounsfield value corresponding to blood;
• and
• locally thinning the 3D segmentation of the vessel to ensure that only one branch is segmented.

A computer program product, comprising:

• a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising:
• computer readable program code configured to perform the steps of:
• visualizing a three-dimensional (3D) segmented bronchial tree;
• receiving a user input, wherein the user input corresponds to a click-point that identifies an airway and the click-point is outside of the 3D segmented bronchial tree;
• locally segmenting the selected airway and determining its orientation/direction;
• computing a two-dimensional (2D) cross-section of the airway perpendicular to the airway's long axis;
• and
• displaying an image of the 2D cross-section of the airway perpendicular to the airway's long axis,
• wherein the selected airway is locally segmented by:
• creating a volume of interest (VOI) around a point of the 3D segmented bronchial tree, wherein the point identifies the selected airway;
• performing a 3D segmentation of the airway within the VOI, wherein the 3D segmentation of the airway includes minimum and maximum thresholds based on a neighborhood of the click-point, wherein the minimum threshold is set to prevent the 3D segmentation from expanding into a bifurcation and the maximum threshold is a Hounsfield value corresponding to air;
• and
• locally thinning the 3D segmentation of the airway to ensure that only one branch is segmented.