Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A method of segmenting one or more soft tissue organs in a 3-D medical image includes registering the image to a bone atlas which also lists landmarks of the organs. Voxels of the image are identified as corresponding to listed landmarks of the organs, based on their position relative to one or more known bones in the registered image. At least one organ is segmented using at least one of the voxels identified as belonging to that organ as a starting point.

Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A method of analyzing structure of a network of vessels in a medical image, comprising: receiving the medical image depicting the network of vessels; obtaining a mask of the network of vessels in the image; and generating a non-forest graph mapping a plurality of paths of vessels in the network to directed paths in the graph, with each edge in the graph either directed to indicate a known direction of flow in the corresponding vessel, or undirected to indicate a lack of knowledge of direction of flow in the corresponding vessel, and with all directed edges in a path directed in a same direction as the path.

Abstract: A method for automated segmentation of a blood vessel of a head and neck of a subject in a medical image, the method comprising: identifying the location of anatomical landmarks in the medical image; identifying regions of interest in the medical image based on the landmarks; segmenting segments of blood vessels in the medical image; classifying at least one of the segments as defining the blood vessel based on its position relative to the landmarks within the regions of interest to create a classified blood vessel; identifying a starting seed for the blood vessel from the classified blood vessel; identifying an ending seed for the blood vessel from the classified blood vessel; segmenting the blood vessel between the starting seed and the ending seed; and defining a path between the starting seed and the ending seed.

Abstract: A method, performed by a computer, of facilitating selection by a user of one path from a set of directed paths in an image, partly overlapping but not constituting a directed tree structure, each path beginning at a root and ending at an end point, the method comprising: a) receiving from the user an indication of a specified location in the image; b) identifying a specified path part that best matches the indication of the specified location; and c) suggesting primarily a single path chosen from among two or more paths in the set which include the specified path part, but do not all follow a same route before the specified path part, and at least one of which separates from another path somewhere along its length.

Abstract: An image analysis system comprising: (a) an image input module adapted to receive a medical image; and (b) an analytic module adapted to segment the image, identify a plurality of vertebrae and label each vertebra.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, b) applying a second set of one or more tests to accept seeds as bone seeds, and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image density has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis; b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image density at or near said voxel, with the image density falling sufficiently steeply in both directions along at least one axis; and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, b) applying a second set of one or more tests to accept seeds as bone seeds, and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: A method and system for detecting a boundary between a first tissue and a second tissue in a two-dimensional image. A cost is assigned to each pixel in a detection region indicative of the likelihood that the pixel is not located on the boundary between the first tissue and the second tissue. The boundary between the first and second tissues or organs is a path of voxels of minimal cost in the detection region from among two or more paths, the cost of a path being obtained in a calculation based upon the costs assigned to the voxels of the path. The method may be used to detect a boundary between a first tissue or organ and a second tissue or organ in a three-dimensional scan by applying the method to each of one or more two-dimensional sections and joining the two dimensional boundaries into a three dimensional boundary in the three dimensional scan. The method may also be used in segmenting a three-dimensional body scan.

Abstract: A method, performed by a computer, of facilitating selection by a user of one path from a set of directed paths in an image, partly overlapping but not constituting a directed tree structure, each path beginning at a root and ending at an end point, the method comprising: a) receiving from the user an indication of a specified location in the image; b) identifying a specified path part that best matches the indication of the specified location; and c) suggesting primarily a single path chosen from among two or more paths in the set which include the specified path part, but do not all follow a same route before the specified path part, and at least one of which separates from another path somewhere along its length.

Abstract: A method of analyzing structure of a network of vessels in a medical image, comprising: receiving the medical image depicting the network of vessels; obtaining a mask of the network of vessels in the image; and generating a non-forest graph mapping a plurality of paths of vessels in the network to directed paths in the graph, with each edge in the graph either directed to indicate a known direction of flow in the corresponding vessel, or undirected to indicate a lack of knowledge of direction of flow in the corresponding vessel, and with all directed edges in a path directed in a same direction as the path.

Abstract: A method of segmenting one or more soft tissue organs in a 3-D medical image, the method comprising: a) registering the image to a bone atlas which also lists landmarks of the organs; b) identifying a plurality of voxels as corresponding to listed landmarks of the organs, based on their position relative to one or more known bones in the registered image; and c) segmenting at least one organ using at least one of the voxels identified as belonging to that organ as a starting point.

Abstract: A method of automatically identifying bone components in a medical image data set of voxels, the method comprising: a) applying a first set of one or more tests to accept voxels as belonging to seeds, wherein none of the tests examine an extent to which the image density has a local maximum at or near a voxel and falls steeply going away from the local maximum in both directions along an axis; b) applying a second set of one or more tests to accept seeds as bone seeds, at least one of the tests requiring at least one voxel belonging to the seed to have a local maximum in image density at or near said voxel, with the image density falling sufficiently steeply in both directions along at least one axis; and c) expanding the bone seeds into bone components by progressively identifying candidate bone voxels, adjacent to the bone seeds or to other previously identified bone voxels, as bone voxels, responsive to predetermined criteria which distinguish bone voxels from voxels of other body tissue.

Abstract: An image analysis system comprising: (a) an image input module adapted to receive a medical image; and (b) an analytic module adapted to segment the image, identify a plurality of vertebrae and label each vertebra.

Abstract: A method and system for detecting a boundary between a first tissue and a second tissue in a two-dimensional image. A cost is assigned to each pixel in a detection region indicative of the likelihood that the pixel is not located on the boundary between the first tissue and the second tissue. The boundary between the first and second tissues or organs is a path of voxels of minimal cost in the detection region from among two or more paths, the cost of a path being obtained in a calculation based upon the costs assigned to the voxels of the path. The method may be used to detect a boundary between a first tissue or organ and a second tissue or organ in a three-dimensional scan by applying the method to each of one or more two-dimensional sections and joining the two dimensional boundaries into a three dimensional boundary in the three dimensional scan. The method may also be used in segmenting a three-dimensional body scan.