Abstract: By example, a method for training a model to segment test images, wherein the test images comprise ultrasound image data, includes: receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers; processing the first plurality of plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapting a trained model; and receiving, at a supervised learning framework, the trained model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the livers, and responsively adapting the trained model.

Abstract: By example, a method includes: receiving ultrasound image data of a patient, including a segmented region corresponding to a liver; automatically identifying an area for a region of interest within the segmented region, wherein the region of interest corresponds to a region in the liver for performing shear-wave elastography; and presenting, on a display, the ultrasound image data and the area for the region of interest.

Abstract: Systems and methods are provided for semi-automated, portable, ultrasound guided cannulation. The systems and methods provide for image analysis to provide for segmentation of vessels of interest from image data. The image analysis provides for guidance for insertion of a cannulation system into a subject which may be accomplished by a non-expert based upon the guidance provided. The guidance may include an indicator or a mechanical guide to guide a user for inserting the vascular cannulation system into a subject to penetrate the vessel of interest.

Abstract: Systems and methods are provided for semi-automated, portable, ultrasound guided cannulation. The systems and methods provide for image analysis to provide for segmentation of vessels of interest from image data. The image analysis provides for guidance for insertion of a cannulation system into a subject which may be accomplished by a non-expert based upon the guidance provided. The guidance may include an indicator or a mechanical guide to guide a user for inserting the vascular cannulation system into a subject to penetrate the vessel of interest.

Abstract: By example, a method includes: receiving ultrasound image data of a patient, including a segmented region corresponding to a liver; automatically identifying an area for a region of interest within the segmented region, wherein the region of interest corresponds to a region in the liver for performing shear-wave elastography; and presenting, on a display, the ultrasound image data and the area for the region of interest.

Abstract: By example, a method for training a model to segment test images, wherein the test images comprise ultrasound image data, includes: receiving, at a self-supervised learning framework, a first plurality of training images, wherein the first plurality of training images include ultrasound data corresponding to patients' livers; processing the first plurality of plurality of training images with a learning algorithm of the self-supervised learning framework, and responsively adapting a trained model; and receiving, at a supervised learning framework, the trained model and a second plurality of training images, wherein the second plurality of training images include ultrasound data corresponding to patients' livers and annotations of the livers, and responsively adapting the trained model.

Abstract: Systems and methods for transmitting non-diffracting acoustic beams are presented herein. In one example, a method for transmitting a non-diffracting acoustic beam with an ultrasound transducer that includes a plurality of transducer elements includes determining a transmit delay function and a transmit apodization function for the ultrasound transducer based on a target axial pressure profile for the acoustic beam for a given configuration of the ultrasound transducer and controlling the ultrasound transducer to transmit the acoustic beam by sending electrical signals to the plurality of transducer elements based on the transmit delay function and the transmit apodization function.

Abstract: A method for remote intervention for a subject includes acquiring an image of a region of interest of the subject using an interventional device positioned on the subject and an image acquisition system. The region of interest includes a target structure and the subject is located at a first site. The method further includes analyzing the acquired image using an image analysis module to identify and label the target structure in the region of interest and transmitting the labelled image from the first site to a second site for expert review. The second site is remote from the first site. The method further includes receiving a command signal at the first site from the second site where the command signal is generated based on the expert review of the labelled image and configured to control an action of the interventional device. In some embodiments, the method may further include analyzing the acquired image to determine a pathway to the vessel that avoids critical structures.

Abstract: A system and method for automatically estimating a hepatorenal index (HRI) from ultrasound images s provided. The method includes acquiring a sequence of ultrasound image data until a desired ultrasound image view is obtained. The method includes segmenting, a liver and a renal cortex in the obtained ultrasound image view. The method includes identifying valid samples in the liver and the renal cortex in the obtained ultrasound image view by excluding invalid samples. The method includes automatically positioning a liver region of interest and a renal cortex region of interest in the obtained ultrasound image view based on the valid samples and at least one criterion. The method includes determining an HRI and causing a display system to present the HRI.

Abstract: Systems and methods are provided for semi-automated, portable, ultrasound guided cannulation. The systems and methods provide for image analysis to provide for segmentation of vessels of interest from image data. The image analysis provides for guidance for insertion of a cannulation system into a subject which may be accomplished by a non-expert based upon the guidance provided. The guidance may include an indicator or a mechanical guide to guide a user for inserting the vascular cannulation system into a subject to penetrate the vessel of interest.

Abstract: A system and method for automatically estimating a hepatorenal index (HRI) from ultrasound images s provided. The method includes acquiring a sequence of ultrasound image data until a desired ultrasound image view is obtained. The method includes segmenting, a liver and a renal cortex in the obtained ultrasound image view. The method includes identifying valid samples in the liver and the renal cortex in the obtained ultrasound image view by excluding invalid samples. The method includes automatically positioning a liver region of interest and a renal cortex region of interest in the obtained ultrasound image view based on the valid samples and at least one criterion. The method includes determining an HRI and causing a display system to present the HRI.

Abstract: Systems and methods are provided for semi-automated, portable, ultrasound guided cannulation. The systems and methods provide for image analysis to provide for segmentation of vessels of interest from image data. The image analysis provides for guidance for insertion of a cannulation system into a subject which may be accomplished by a non-expert based upon the guidance provided. The guidance may include an indicator or a mechanical guide to guide a user for inserting the vascular cannulation system into a subject to penetrate the vessel of interest.

Abstract: Systems and methods are provided for objective, noninvasive staging of diffuse liver disease, and other diseases, from ultrasound shear wave elastography (“SWE”). A liver fibrosis staging SWE image analysis algorithm or toolkit may be used to detect the presence of liver disease and may integrate demographic, clinical, laboratory, conventional sonographic, SWE data, and the like into a combined disease diagnosis decision support model, such as for the diagnosis of high risk liver disease. Advanced image processing methods may be used to extract fibrosis pattern information from SWE and ultrasound (US) image data and this can be used to augment stiffness-based liver fibrosis staging methods.

Abstract: Quantitative measurement of parameters pertaining to scan technique, such as applied force and transducer angulation, is used to identify ultrasound users at increased risk for injury. Usage patterns known to increase risk of injury can also be used to identify poor scanning techniques during a scan and provide in-scan feedback to an ultrasound user to change scanning behavior. Data regarding scan technique can be aggregated to determine the risk of injury to an individual ultrasound machine operator. This data may be used by ultrasound machine operators and ultrasound managers to prevent injury by pre-emptively correcting scan technique associated with injury.