Abstract: Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Abstract: Systems and methods for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.

Abstract: A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Abstract: Systems and methods are provided for three dimensional depth reconstruction of vessels in two dimensional medical images. A medical image comprising braches of one or more vessels is received. A branch overlap image channel that represents a pixelwise probability that the branches overlap is generated. A set of branch orientation image channels are generated. Each branch orientation image channel is associated with one of a plurality of orientations. Each branch orientation image channel representing a image channel represents a pixelwise probability that the branches are oriented in its associated orientation. A multi-channel depth image is generated based on the branch overlap image channel and the set of branch orientation image channels. Each channel of the multi-channel depth image comprises portions of the branches corresponding to a respective depth.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: Systems and methods are provided for automatic segmentation of a vessel. A sequence of image slices containing a vessel is acquired. Features maps are generated for each of the image slices using a trained fully convolutional neural network. A trained bi-directional recurrent neural network generates a segmented image based on the feature maps.

Abstract: Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Abstract: A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Abstract: A computer-implemented method for editing image processing results includes performing one or more image processing tasks on an input image using an iterative editing process. The iterative editing process is executed until receiving a user exit request. Each iteration of the iterative editing process comprises using a first machine learning model to generate a plurality of processed images. Each processed image corresponds to a distinct set of processing parameters. The iterative editing process further comprises presenting the plurality of processed images to a user on a display and receiving a user response comprising (i) an indication of acceptance of one or more of the processed images, (ii) an indication of rejection of all of the processed images, or (iii) the user exit request. Following the iterative editing process clinical tasks are performed using at least one of the processed images generated immediately prior to receiving the user exit request.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: Systems and methods are provided for automatic segmentation of a vessel. A sequence of image slices containing a vessel is acquired. Features maps are generated for each of the image slices using a trained fully convolutional neural network. A trained bi-directional recurrent neural network generates a segmented image based on the feature maps.

Abstract: A computer-implemented method for editing image processing results includes performing one or more image processing tasks on an input image using an iterative editing process. The iterative editing process is executed until receiving a user exit request. Each iteration of the iterative editing process comprises using a first machine learning model to generate a plurality of processed images. Each processed image corresponds to a distinct set of processing parameters. The iterative editing process further comprises presenting the plurality of processed images to a user on a display and receiving a user response comprising (i) an indication of acceptance of one or more of the processed images, (ii) an indication of rejection of all of the processed images, or (iii) the user exit request. Following the iterative editing process clinical tasks are performed using at least one of the processed images generated immediately prior to receiving the user exit request.

Abstract: A method and apparatus for extracting centerline representations of vascular structures in medical images is disclosed. A vessel orientation tensor for each of a plurality of voxels associated with the target vessel, such as a coronary artery, in a medical image, such as a coronary tomography angiography (CTA) image, using a trained vessel orientation tensor classifier. A flow field is estimated for the plurality of voxels associated with the target vessel in the medical image based on the vessel orientation tensor estimated for each of the plurality of voxels. A centerline of the target vessel is extracted based on the estimated flow field for the plurality of vessels associated with the target vessel in the medical image by detecting a path that carries maximum flow.

Abstract: A method and apparatus for machine learning based detection of vessel orientation tensors of a target vessel from a medical image is disclosed. For each of a plurality of voxels in a medical image, such as a computed tomography angiography (CTA), features are extracted from sampling patches oriented to each of a plurality of discrete orientations in the medical image. A classification score is calculated for each of the plurality of discrete orientations at each voxel based on the features extracted from the sampling patches oriented to each of the plurality of discrete orientations using a trained vessel orientation tensor classifier. A vessel orientation tensor at each voxel is calculated based on the classification scores of the plurality of discrete orientations at that voxel.

Abstract: A method and apparatus for learning based classification of vascular branches to distinguish falsely detected branches from true branches is disclosed. A plurality of overlapping fixed size branch segments are sampled from branches of a detected centerline tree of a target vessel extracted from a medical image of a patient. A plurality of 1D profiles are extracted along each of the overlapping fixed size branch segments. A probability score for each of the overlapping fixed size branch segments is calculated based on the plurality of 1D profiles extracted for each branch segment using a trained deep neural network classifier. The trained deep neural network classifier may be a convolutional neural network (CNN) trained to predict a probability of a branch segment being fully part of a target vessel based on multi-channel 1D input.

Abstract: A method and apparatus for machine learning based detection of vessel orientation tensors of a target vessel from a medical image is disclosed. For each of a plurality of voxels in a medical image, such as a computed tomography angiography (CTA), features are extracted from sampling patches oriented to each of a plurality of discrete orientations in the medical image. A classification score is calculated for each of the plurality of discrete orientations at each voxel based on the features extracted from the sampling patches oriented to each of the plurality of discrete orientations using a trained vessel orientation tensor classifier. A vessel orientation tensor at each voxel is calculated based on the classification scores of the plurality of discrete orientations at that voxel.

Abstract: A method and apparatus for learning based classification of vascular branches to distinguish falsely detected branches from true branches is disclosed. A plurality of overlapping fixed size branch segments are sampled from branches of a detected centerline tree of a target vessel extracted from a medical image of a patient. A plurality of 1D profiles are extracted along each of the overlapping fixed size branch segments. A probability score for each of the overlapping fixed size branch segments is calculated based on the plurality of 1D profiles extracted for each branch segment using a trained deep neural network classifier. The trained deep neural network classifier may be a convolutional neural network (CNN) trained to predict a probability of a branch segment being fully part of a target vessel based on multi-channel 1D input.

Abstract: A method and apparatus for extracting centerline representations of vascular structures in medical images is disclosed. A vessel orientation tensor for each of a plurality of voxels associated with the target vessel, such as a coronary artery, in a medical image, such as a coronary tomography angiography (CTA) image, using a trained vessel orientation tensor classifier. A flow field is estimated for the plurality of voxels associated with the target vessel in the medical image based on the vessel orientation tensor estimated for each of the plurality of voxels. A centerline of the target vessel is extracted based on the estimated flow field for the plurality of vessels associated with the target vessel in the medical image by detecting a path that carries maximum flow.

Abstract: Phase unwrapping is provided for phase contrast magnetic resonance (MR) imaging. The velocity values are unaliased. For a given location over time, a path over time through a directed graph of possible velocities at each time is determined by minimization of derivatives over time. The possible velocities are based on the input velocity, the input velocity wrapped in a positive direction, and the input velocity wrapped in a negative direction, so the selection to create the minimum cost path represents unaliasing of any aliased velocities.