Abstract: Methods and systems are provided for removing visual artifacts from a medical image acquired during a scan of an object, such as a patient. In one example, a method for an image processing system comprises receiving a medical image; performing a wavelet decomposition on image data of the medical image; performing one or more 2-D Fourier transforms on wavelet coefficients resulting from the wavelet decomposition; removing image artifacts from the Fourier coefficients determined from the 2-D Fourier transforms using a filter; reconstructing the medical image using the filtered Fourier coefficients; and displaying the reconstructed medical image on a display device of the image processing system.

Abstract: Systems and methods are provided for quantifying uncertainty of segmentation mask predictions made by machine learning models, where the uncertainty may be used to streamline an anatomical measurement workflow by automatically identifying less certain caliper placements. In one example, the current disclosure teaches receiving an image including a region of interest, determining a segmentation mask for the region of interest using a trained machine learning model, placing a caliper at a position within the image based on the segmentation mask, determining an uncertainty of the position of the caliper, and responding to the uncertainty of the position of the caliper being greater than a pre-determined threshold by displaying a visual indication of the position of the caliper via a display device and prompting a user to confirm or edit the position of the caliper.

Abstract: Various methods and systems are provided for bicuspid valve detection with ultrasound imaging. In one embodiment, a method comprises acquiring ultrasound video of a heart over at least one cardiac cycle, identifying frames in the ultrasound video corresponding to at least one cardiac phase, and classifying a cardiac structure in the identified frames as a bicuspid valve or a tricuspid valve. A generative model such as a variational autoencoder trained on ultrasound image frames at the at least one cardiac phase may be used to classify the cardiac structure. In this way, relatively rare occurrences of bicuspid aortic valves may be automatically detected during regular cardiac ultrasound screenings.

Abstract: Systems and methods for automatically segmenting and detecting a menstrual cycle phase in ultrasound images of anatomical structures that change over a patient menstrual cycle are provided. The method includes acquiring, by an ultrasound probe of an ultrasound system, an ultrasound image of a region of interest having an anatomical structure that changes over a patient menstrual cycle. The method includes automatically segmenting, by at least one processor of the ultrasound system, an anatomical structure depicted in the ultrasound image. The method includes automatically predicting, by the at least one processor, a menstrual cycle phase based on the segmentation of the anatomical structure. The method includes causing, by the at least one processor, a display system to present at least one rendering of the segmented anatomical structure and the predicted menstrual cycle phase.

Abstract: Systems and methods are provided for quantifying uncertainty of segmentation mask predictions made by machine learning models, where the uncertainty may be used to streamline an anatomical measurement workflow by automatically identifying less certain caliper placements. In one example, the current disclosure teaches receiving an image including a region of interest, determining a segmentation mask for the region of interest using a trained machine learning model, placing a caliper at a position within the image based on the segmentation mask, determining an uncertainty of the position of the caliper, and responding to the uncertainty of the position of the caliper being greater than a pre-determined threshold by displaying a visual indication of the position of the caliper via a display device and prompting a user to confirm or edit the position of the caliper.

Abstract: A method and ultrasound imaging system includes generating a cine including a plurality of cardiac views based on the cardiac ultrasound data, segmenting a plurality of cardiac chambers from each of the plurality of cardiac images, and automatically determining a cardiac chamber area for each of the plurality of cardiac chambers. The method and ultrasound imaging system includes displaying the cine on a display device and displaying a plurality of single trace curves on the display device at the same time as the cine to provide feedback regarding an acquisition quality of the cine, wherein each of the single trace curves represents the cardiac chamber area for a different one of the plurality of cardiac chambers over the plurality of cardiac cycles.

Abstract: Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

Abstract: Various methods and systems are provided for bicuspid valve detection with ultrasound imaging. In one embodiment, a method comprises acquiring ultrasound video of a heart over at least one cardiac cycle, identifying frames in the ultrasound video corresponding to at least one cardiac phase, and classifying a cardiac structure in the identified frames as a bicuspid valve or a tricuspid valve. A generative model such as a variational autoencoder trained on ultrasound image frames at the at least one cardiac phase may be used to classify the cardiac structure. In this way, relatively rare occurrences of bicuspid aortic valves may be automatically detected during regular cardiac ultrasound screenings.

Abstract: Systems and method for domain adaptation using pseudo-labelling and model certainty quantification for video data are provided. The method includes obtaining a source data and a target data each comprising a plurality of frames for processing by a machine learning module. The method comprises testing the target data to identify if a minimum number of frames exhibit a frame confidence score based on the source data and identifying salient region within the target data and measuring a degree of spatial consistency of the salient region over time.

Abstract: A method and ultrasound imaging system includes generating a cine including a plurality of cardiac views based on the cardiac ultrasound data, segmenting a plurality of cardiac chambers from each of the plurality of cardiac images, and automatically determining a cardiac chamber area for each of the plurality of cardiac chambers. The method and ultrasound imaging system includes displaying the cine on a display device and displaying a plurality of single trace curves on the display device at the same time as the cine to provide feedback regarding an acquisition quality of the cine, wherein each of the single trace curves represents the cardiac chamber area for a different one of the plurality of cardiac chambers over the plurality of cardiac cycles.

Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.

Abstract: Systems, machine-readable media, and methods for ultrasound imaging can include acquiring three-dimensional data for one or more patient data sets and generating a three-dimensional environment based on one or more transition areas identified between a plurality of volumes of the three-dimensional data. A method can also include generating a set of probe guidance instructions based at least in part on the one or more transition areas and the plurality of volumes of the three-dimensional data, and acquiring, using an ultrasound probe, a first frame of two-dimensional data for a patient. The method can also include executing the set of probe guidance instructions to provide probe feedback for acquiring at least a second frame of two-dimensional data.

Abstract: Methods and systems are provided for sequentially selecting scan parameter values for ultrasound imaging. In one example, a method includes selecting a first parameter value for the a first scan parameter based on an image quality of each ultrasound image of a first plurality of ultrasound images of an anatomical region, each ultrasound image of the first plurality of ultrasound images having a different parameter value for the first scan parameter, selecting a second parameter value for a second scan parameter based on an image quality of each ultrasound image of a second plurality of ultrasound images of the anatomical region, each ultrasound image of the second plurality of ultrasound images having a different parameter value for the second scan parameter, and applying the first parameter value for the first scan parameter and the second parameter value for the second scan parameter to one or more additional ultrasound images.