Abstract: A machine learning model is described that is trained without labels to predict a motion field between a pair of images. The trained model can be applied to a distinguished pair of images to predict a motion field between the distinguished pair of images.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: A facility for processing a medical imaging image is described. The facility applies each of a number of constituent models making up an ensemble machine learning models to the image to produce a constituent model result that predicts a value for each pixel of the image. The facility aggregates the results produced by the constituent models of the plurality to determine a result of the ensemble machine learning model. For each of the pixels of the accessed image, the facility determines a measure of variation among the values predicted for the pixel among the constituent models. Facility determines a confidence measure for the ensemble machine learning model result based at least in part on for how many of the pixels of the accessed image a variation measure is determined that exceeds a variation threshold.

Abstract: A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

Abstract: A facility for processing a medical imaging image is described. The facility applies to the image a first machine learning model trained to recognize a view to which an image corresponds, and a second machine learning model trained to identify any of a set of anatomical features visualized in an image. The facility accesses a list of permitted anatomical features for images corresponding to the recognized view, and filters the identified anatomical features to exclude any not on the accessed list. The facility causes the accessed image to be displayed, overlaid with a visual indication of each of the filtered identified anatomical features.

Abstract: A facility for processing a medical imaging image is described. The facility applies each of a number of constituent models making up an ensemble machine learning models to the image to produce a constituent model result that predicts a value for each pixel of the image. The facility aggregates the results produced by the constituent models of the plurality to determine a result of the ensemble machine learning model. For each of the pixels of the accessed image, the facility determines a measure of variation among the values predicted for the pixel among the constituent models. Facility determines a confidence measure for the ensemble machine learning model result based at least in part on for how many of the pixels of the accessed image a variation measure is determined that exceeds a variation threshold.

Abstract: A machine learning model is described that is trained without labels to predict a motion field between a pair of images. The trained model can be applied to a distinguished pair of images to predict a motion field between the distinguished pair of images.

Abstract: Systems and methods for automated physiological parameter estimation from ultrasound image sequences are provided. An ultrasound system includes an ultrasound imaging device configured to acquire a sequence of ultrasound images of a patient. An anatomical structure recognition module includes processing circuitry configured to receive the acquired sequence of ultrasound images from the ultrasound imaging device, and automatically recognize an anatomical structure in the received sequence of ultrasound images. A physiological parameters estimation module includes processing circuitry configured to automatically estimate one or more physiological parameters associated with the recognized anatomical structure.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Automated ultrasound image labeling and quality grading systems and methods are provided. An ultrasound system includes an ultrasound imaging device configured to acquire ultrasound images of a patient. An anatomical structure recognition and labeling module receives the acquired ultrasound images from the ultrasound imaging device, and automatically recognizes anatomical structures in the received ultrasound images. The anatomical structure recognition and labeling module automatically labels the anatomical structures in the images with information that identifies the anatomical structures. The acquired ultrasound images and the labeled anatomical structures are displayed on a display of the ultrasound imaging device.

Abstract: Mechanisms are provided for evaluating the normality of a medical condition of a patient based on a medical image. A medical image segmentation receives a medical image and segments the medical image to generate an extracted contour representing an anatomical feature. The medical image segmentation engine correlates the extracted contour with a template shape corresponding to the anatomical feature. A feature extraction engine extracts one or more features from a region of the medical image corresponding to the template shape. A normality classification engine performs a normality classification operation on the extracted one or more features to generate a normality score for the medical image and outputs the normality score to a computing device.

Abstract: Mechanisms are provided for evaluating the normality of a medical condition of a patient based on a medical image. A medical image segmentation receives a medical image and segments the medical image to generate an extracted contour representing an anatomical feature. The medical image segmentation engine correlates the extracted contour with a template shape corresponding to the anatomical feature. A feature extraction engine extracts one or more features from a region of the medical image corresponding to the template shape. A normality classification engine performs a normality classification operation on the extracted one or more features to generate a normality score for the medical image and outputs the normality score to a computing device.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: Mechanisms are provided to implement an automated echocardiograph measurement extraction system. The automated echocardiograph measurement extraction system receives medical imaging data comprising one or more medical images and inputs the one or more medical images into a deep learning network. The deep learning network automatically processes the one or more medical images to generate an extracted echocardiograph measurement vector output comprising one or more values for echocardiograph measurements extracted from the one or more medical images. The deep learning network outputs the extracted echocardiograph measurement vector output to a medical image viewer.

Abstract: A method for acquiring analytical information may be provided. The method may include providing a database configured to store user specific data and providing a user interface for displaying, inputting, or analyzing the data. A customer interface may be provided to allow for customer interaction with a business, retailer, or service provider. Customer data may be measured through online interactions, signals from mobile devices, and in-store interactions. User specific data and general customer data may be compiled in the database and feedback may be generated based on the data and presented through the user interface.

Abstract: A method for acquiring analytical information may be provided. The method may include providing a database configured to store user specific data and providing a user interface for displaying, inputting, or analyzing the data. A customer interface may be provided to allow for customer interaction with a business, retailer, or service provider. Customer data may be measured through online interactions, signals from mobile devices, and in-store interactions. User specific data and general customer data may be compiled in the database and feedback may be generated based on the data and presented through the user interface.

Abstract: A method for determining the type of a digital versatile disc (DVD) so that a DVD+ can be distinguished from a DVD?. The main steps for determining the type of a DVD include setting up related parameters, reading a general wobble signal or a wobble signal within the general wobble signal from a DVD to obtain a peak hold value and a trough hold value. The peak and trough hold values of the general wobble signal are read to derive a peak-to-peak voltage. Finally, if the peak-to-peak voltage is greater than a predetermined threshold value, the DVD is classified as a DVD?, otherwise it is classified as a DVD+. Furthermore, a DVD-ROM disc can be determined.

Abstract: A method and an apparatus for determining types of digital versatile discs (DVDs) are provided. The method includes setting related parameters, acquiring a wobble signal from a DVD, setting a frequency dividing factor, and dividing the frequency of the wobble signal by the frequency dividing factor. Then, the time required for the digital versatile disc to spin one round, which is driven by a spindle motor of a disc player, is set as a unit time to calculate the frequency of the frequency-divided wobble signal. Finally, the type of the digital versatile disc is determined according to the frequency of the wobble signal.