Abstract: A system and method for controlling a standalone computer through a separate touch screen input device.

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 of determining a residue function in brain tissue, from medical images acquired after introducing contrast agent into the blood, correcting for contrast agent leakage into the tissue, comprising: a) providing time signals indicating contrast agent concentration for leaking voxels, a time signal indicating average contrast agent concentration for non-leaking voxels, and an artery input function, all derived from the images; b) fitting the leaking voxel signals to a model time signal with a free parameter for leakage rate, the model assuming that the concentration of contrast agent perfusing through a leaking voxel has a same shape as a function of time as the average contrast agent concentration for non-leaking voxels; c) using the best fit leakage rate parameter to make a correction for leakage to the leaking voxel signals; and d) deconvolving the corrected signals from the artery input function, to find the residue function.

Abstract: A method for segmenting a medical image. The method comprises providing a registration of a medical image depicting a plurality of bones with a skeletal atlas mapping an exemplary skeletal structure having a plurality of exemplary bones, identifying at least one component having at least one anatomical characteristic indicative of a predefined organ in the medical image, each the at least one component depicting at least one of the plurality of bones, and segmenting at least one region of interest (ROI) around the at least one component within the medical image according to at least one respective the exemplary bone pertaining to the predefined organ.

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 system and method for controlling a standalone computer through a separate touch screen input device.

Abstract: A method of processing a medical image comprising an organ, the method comprising: obtaining a medical image; automatically estimating one or more organ intensity characteristics in the image, from contents of a region of the image that appears to correspond, at least in part, to at least a portion of the organ; providing a plurality of sets of organ intensity characteristics, each set a different example of possible intensity characteristics of the organ; choosing one of the plurality of sets, that has organ intensity characteristics that provide a better match than one or more other sets to the estimated organ intensity characteristics of the image; setting values of one or more image processing parameters based on the organ intensity characteristics of the chosen set; and automatically processing the image using said values of image processing parameters.

Abstract: A method of segmenting a target organ of the body in medical images, the method comprising: providing a probabilistic atlas with probabilities for a presence of the target organ at different locations relative to a bounding region of the target organ; providing one or more medical test images; for each test image, identifying, at least provisionally, a location of a bounding region for the target organ in the test image; and for each test image, using the probabilities from the probabilistic atlas to segment the target organ in the test image, with the segmenting performed by a data processor.

Abstract: Spatial segmentation of lymph nodes in a 3-D medical image is automatically determined, based on a set of inputs provided by a user which define a low number of initial conditions for segmentation. In some embodiments, the automation comprises producing a lymph node segmentation from the 3-D image based on a 2-D image slice and a representative line segment on that slice. In some embodiments, segmentation comprises a two tiered approach (2-D segmentation, followed by 3-D segmentation) based on adaptation of the level set framework to the particular conditions of lymph node segmentation.

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 a rapid automated presentation of at least two radiological data sets of a patient, comprising, (a) automatically registering the data sets in 3D space; and (b) concurrently presenting substantially matching anatomical regions in each data set.

Abstract: A system and method for performing DSA (digital subtraction angiography), which does not require a non-enhanced or “mask” image to be obtained.

Abstract: A system and method for automatic detection of an object feature, such as a lesion, across a plurality of sets of image data, taken from the same subject, which may optionally be a human patient but which may also optionally be any type of animal or a non-biological subject.

Abstract: A method for error correction for segmented three dimensional image data. The method includes receiving segmented three dimensional image data, the segmented three dimensional image data being divided into a plurality of slices; correcting at least one contour of the segmented three dimensional image data on at least one slice according to a command from a user to form a corrected contour; and automatically interpolating a correction represented by the corrected contour to a plurality of slices of the segmented three dimensional image data.

Abstract: A method for registering a medical image with a model mapping a skeletal structure. The method comprises receiving a medical image depicting a plurality of bones, providing a statistical model mapping a reference skeletal structure having a plurality of reference anatomical elements, and registering medical image with the statistical model according to at least one constraint defined by the plurality of reference anatomical elements.

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 system and method for controlling a standalone computer through a separate touch screen input device.

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 centerline determination for a tubular tissue in a medical image data set defined in a data space, comprising receiving at least one start point and one end point inside a tubular tissue volume; automatically determining a path between said points that remains inside said volume; automatically segmenting said tubular tissue using said path; and automatically determining a centerline for said tubular tissue from said segmentation, wherein said receiving, said determining a path and said segmenting, said determining a centerline are all performed on a same data space of said medical image data set.