Abstract: An anatomical structure is segmented from images generated in an anatomical scan. A user input device is configured for manually identifying two or more seed points on a plurality of the images. An image processor is configured to identify an optimal path through seed points on the plurality of images with a graph search. The optimal path between corresponding seed points on different images defines a boundary contour, and the anatomical structure includes two or more boundary contour. The image processor is further configured to interpolate the boundary contours over a three-dimensional volume using cardinal splines.

Abstract: Methods and apparatus are disclosed to label radiology images are disclosed. An example apparatus, includes: means for obtaining user input identifying a vertebra on a spinal image; means for generating first annotations on the spinal image based on: 1) the user input; 2) connected regions on the spinal image; and 3) a number of viewable vertebrae on the spinal image; means for generating second annotations on the spinal image based on: 1) the error; 2) second connected regions on the spinal image; and 3) the number of viewable vertebrae on the spinal image, the second connected regions on the spinal image being determined based on second contextual-information features that account for the error; means for displaying the spinal image including the second annotations; means for validating the second annotations in association with the spinal image; and means for storing the second annotations in association with the spinal image.

Abstract: An apparatus and method for generating a fused scan image from a plurality of anatomical scan images of a patient. A tracking system is used to track the physical position and orientation of a scanner transducer, such as an ultrasound probe, which is used to obtain the anatomical scan images. The tracking system may also be used to track markers positioned on the patient for tracking anatomical movement of the patient. An image-processing based fusion is applied to the plurality of anatomical scan images based on the tracked position and orientation of the scanner transducer and the tracked patient anatomical movement to generate a fused scan image. The anatomical movement may comprise respiratory movement of the patient. An electrocardiogram (ECG) signal from the patient may be used to time synchronize tracking information generated by the tracking system with the plurality of anatomical scan images.

Abstract: An anatomical structure is segmented from images generated in an anatomical scan. A user input device is configured for manually identifying two or more seed points on a plurality of the images. An image processor is configured to identify an optimal path through seed points on the plurality of images with a graph search. The optimal path between corresponding seed points on different images defines a boundary contour, and the anatomical structure includes two or more boundary contour. The image processor is further configured to interpolate the boundary contours over a three-dimensional volume using cardinal splines.

Abstract: Methods and apparatus are disclosed to label radiology images are disclosed. An example apparatus, includes: means for obtaining user input identifying a vertebra on a spinal image; means for generating first annotations on the spinal image based on: 1) the user input; 2) connected regions on the spinal image; and 3) a number of viewable vertebrae on the spinal image; means for generating second annotations on the spinal image based on: 1) the error; 2) second connected regions on the spinal image; and 3) the number of viewable vertebrae on the spinal image, the second connected regions on the spinal image being determined based on second contextual-information features that account for the error; means for displaying the spinal image including the second annotations; means for validating the second annotations in association with the spinal image; and means for storing the second annotations in association with the spinal image.

Abstract: Methods and apparatus are disclosed to label radiology images. An example system includes a processor and a database. In response to receiving a first user input via a data input, the processor is to generate first annotations on a spinal image. The processor is to cause the spinal image having the first annotations to be displayed at a user interface. The processor is to prompt a user to provide second user input identifying an error in the first annotations. In response to receiving the second user input, the processor is to generate second annotations on the spinal image. The second annotations is to correct the error in the first annotations.

Abstract: A time series of image frames in an anatomical scan is segmented. An image frame k in the time series of image frames is manually segmented. A non-rigid registration is then performed between the image frame k and a next image frame k+1 in the time series of image frames. A segmentation on the image frame k+1 is computed based on the non-rigid registration. Each subsequent image frame k+n in the time series of image frames is iteratively segmented using non-rigid registration with the segmented previous image frame k+(n?1) in the time series of image frames.

Abstract: A time series of image frames in an anatomical scan is segmented. An image frame k in the time series of image frames is manually segmented. A non-rigid registration is then performed between the image frame k and a next image frame k+1 in the time series of image frames. A segmentation on the image frame k+1 is computed based on the non-rigid registration. Each subsequent image frame k+n in the time series of image frames is iteratively segmented using non-rigid registration with the segmented previous image frame k+(n?1) in the time series of image frames.

Abstract: Methods and apparatus are disclosed to methods and apparatus to generate three-dimensional spinal renderings. An example computer-implemented method includes receiving initial user input identifying a vertebra on a spinal image. In response to receiving the initial user input, simultaneously detecting inter-vertebral discs and vertebral bodies. The computer-implemented method includes displaying the detected inter-vertebral discs and vertebral bodies.

Abstract: Methods and apparatus are disclosed to automatically detect vertebrae boundaries in a spine image. A method to detect a vertebra in a spine image is described, the method comprising generating a rectangle approximation of a reference vertebra. The method also including identifying a mask similar to the rectangle approximation and labeling a mask region in the mask. The method also including comparing the mask region to the rectangle approximation and detecting a vertebra in the spine image based on the comparison.

Abstract: Methods and apparatus are disclosed to methods and apparatus to generate three-dimensional spinal renderings. An example computer-implemented method includes receiving initial user input identifying a vertebra on a spinal image. In response to receiving the initial user input, simultaneously detecting inter-vertebral discs and vertebral bodies. The computer-implemented method includes displaying the detected inter-vertebral discs and vertebral bodies.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes using a filter coefficient based on a plurality of cardiac images to characterize motion of a heart; computing an information-theoretic metric from the filter coefficient; and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to track endocardial motion are disclosed. A disclosed example method includes segmenting a plurality of cardiac images of a left ventricle to form respective ones of a plurality of segmented images, updating a plurality of models based on the plurality of segmented images to form respective ones of a plurality of motion estimates for the left ventricle, computing a plurality of probabilities for respective ones of the plurality of models, and computing a weighted sum of the plurality of motion estimates based on the plurality of probabilities, the weighted sum representing a predicted motion of the left ventricle.

Abstract: Methods and apparatus are disclosed to label radiology images. An example system includes a processor and a database. In response to receiving a first user input via a data input, the processor is to generate first annotations on a spinal image. The processor is to cause the spinal image having the first annotations to be displayed at a user interface. The processor is to prompt a user to provide second user input identifying an error in the first annotations. In response to receiving the second user input, the processor is to generate second annotations on the spinal image. The second annotations is to correct the error in the first annotations.

Abstract: Methods and apparatus are disclosed to automatically detect vertebrae boundaries in a spine image. A method to detect a vertebra in a spine image is described, the method comprising generating a rectangle approximation of a reference vertebra. The method also including identifying a mask similar to the rectangle approximation and labeling a mask region in the mask. The method also including comparing the mask region to the rectangle approximation and detecting a vertebra in the spine image based on the comparison.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes using a filter coefficient based on a plurality of cardiac images to characterize motion of a heart; computing an information-theoretic metric from the filter coefficient; and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes adapting a state of a state-space model based on a plurality of cardiac images to characterize motion of a heart, computing an information-theoretic metric from the state of the state-space model, and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes adapting a state of a state-space model based on a plurality of cardiac images to characterize motion of a heart, computing an information-theoretic metric from the state of the state-space model, and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.

Abstract: Example methods, apparatus and articles of manufacture to track endocardial motion are disclosed. A disclosed example method includes segmenting a plurality of cardiac images of a left ventricle to form respective ones of a plurality of segmented images, updating a plurality of models based on the plurality of segmented images to form respective ones of a plurality of motion estimates for the left ventricle, computing a plurality of probabilities for respective ones of the plurality of models, and computing a weighted sum of the plurality of motion estimates based on the plurality of probabilities, the weighted sum representing a predicted motion of the left ventricle.