Abstract: Systems and methods for tissue specific time gain compensation of an ultrasound image are provided. The method comprises acquiring an ultrasound image of a subject and displaying the ultrasound image over a console. The method further comprises selecting by a user a region within the ultrasound image that requires time gain compensation. The method further comprises carrying out time gain compensation of the user selected region of the ultrasound image. The method further comprises identifying a region having a similar texture to the user selected region and carrying out time gain compensation of the user selected region by an artificial intelligence (AI) based deep learning module.

Abstract: Systems/techniques that facilitate machine learning image analysis based on explicit equipment parameters are provided. In various embodiments, a system can access a medical image generated by a medical imaging device. In various instances, the system can perform, via execution of a machine learning model, an inferencing task on the medical image. In various cases, the machine learning model can receive as input the medical image and a set of equipment parameters. In various aspects, the set of equipment parameters can indicate how the medical imaging device was configured to generate the medical image.

Abstract: Systems/techniques that facilitate improved uncertainty scoring for neural networks via stochastic weight perturbations are provided. In various embodiments, a system can access a trained neural network and/or a data candidate on which the trained neural network is to be executed. In various aspects, the system can generate an uncertainty indicator representing how confidently executable or how unconfidently executable the trained neural network is with respect to the data candidate, based on a set of perturbed instantiations of the trained neural network.

Abstract: Systems and methods for automatically tracking a minimal hiatal dimension plane of an ultrasound volume in real-time during a pelvic floor examination are provided. The method includes acquiring an ultrasound volume of an anatomical region over a time period. The method includes extracting an A-plane from the ultrasound volume and displaying the A-plane. The method includes receiving an OmniView (OV) line overlaid on the A-plane. The method includes rendering an OV-plane based on a position and trajectory of the OV-line and displaying the OV-plane. The method includes automatically identifying key points in regions of interest in the A-plane. The method includes automatically tracking the key points in the regions of interest in the A-plane over the time period to automatically adjust the position and trajectory of the OV-line, the rendering the OV-plane automatically updating over the time period based on adjustments of the position and trajectory of the OV-line.

Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.

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: Methods and systems are provided for an automated ultrasound exam. In one example, a method includes identifying a view plane of interest based on one or more 3D ultrasound images, obtaining a view plane image including the view plane of interest from a 3D volume of ultrasound data of a patient, where the one or more 3D ultrasound images are generated from the 3D volume of ultrasound data, segmenting an anatomical region of interest (ROI) within the view plane image to generate a contour of the anatomical ROI, and displaying the contour on the view plane image.

Abstract: Methods and systems are provided for generating ultrasound probe motion recommendations. In one example, a method includes obtaining an ultrasound image of a source scan plane, the ultrasound image acquired with an ultrasound probe at a first location relative to a patient, entering the ultrasound image as input to a probe recommendation model trained to output a set of recommendations to move the ultrasound probe from the first location to a plurality of additional locations at which a plurality of target scan planes can be imaged, and displaying the set of recommendations on a display device.

Abstract: Methods and systems are provided for determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a method includes selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad, and training a machine learning model with the ECG training data triad.

Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

Abstract: The subject matter discussed herein relates to systems and methods for generating a clinical outcome based on creating a task-specific model associated with processing raw image(s). In one such example, input raw data is acquired using an imaging system, a selection input corresponding to a clinical task is received, and a task-specific model corresponding to the clinical task is retrieved. Using the task-specific model, the raw data is mapped onto an application specific manifold. Based on the mapping of the raw data onto the application specific manifold the clinical outcome is generated, and subsequently providing the clinical outcome for review.

Abstract: Methods and systems are provided for automatically determining a phase shift and noise insensitive similarity metric for electrocardiogram (ECG) beats in a Holter monitor recording. In one embodiment, a deep neural network may be trained to map an ECG beat to a phase shift insensitive and noise insensitive feature space embedding using a training data triad, wherein the training data triad may be produced by a method comprising: selecting a first beat and a second beat recorded via one or more Holter monitors, determining a dynamic time warping (DTW) distance between the first beat and the second beat, setting a similarity label for the first beat and the second beat based on the DTW distance, and storing the first beat, the second beat, and the similarity label, in a location of non-transitory memory as an ECG training data triad.

Abstract: Methods and systems are provided for assessing image quality of ultrasound images. In one example, a method includes determining a probe position quality parameter of an ultrasound image, the probe position quality parameter representative of a level of quality of the ultrasound image with respect to a position of an ultrasound probe used to acquire the ultrasound image, determining one or more acquisition settings quality parameters of the ultrasound image, each acquisition settings quality parameter representative of a respective level of quality of the ultrasound image with respect to a respective acquisition setting used to acquire the ultrasound image, and providing feedback to a user of the ultrasound system based on the probe position quality parameter and/or the one or more acquisition settings quality parameters, the probe position quality parameter and each acquisition settings quality parameter determined based on output from separate image quality assessment models.

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: The present approach relates to a system capable of life-long learning in a deep learning context. The system includes a deep learning network configured to process an input dataset and perform one or more tasks from among a first set of tasks. As an example, the deep learning network may be part of an imaging system, such as a medical imaging system, or may be used in industrial applications. The system further includes a learning unit communicatively coupled to the deep learning network 102 and configured to modify the deep learning network so as to enable it to perform one or more tasks in a second task list without losing the ability to perform the tasks from the first list.

Abstract: Methods and systems are provided for dynamically selecting ultrasound transmits. In one example, a method includes dynamically updating a number of transmit lines and/or a pattern of transmit lines for acquiring an ultrasound image based on a prior ultrasound image and a task to be performed with the ultrasound image, and acquiring the ultrasound image with an ultrasound probe controlled to operate with the updated number of transmit lines and/or the updated pattern of transmit lines.

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 techniques that facilitate generation of model suitability coefficients based on generative adversarial networks and activation maps are provided. In various embodiments, a system can access a deep learning model that is trained on a training dataset. In various instances, the system can compute a model suitability coefficient that indicates whether the deep learning model is suitable for deployment on a target dataset, based on analyzing activation maps associated with the deep learning model. In various aspects, the system can train a generative adversarial network (GAN) to model a distribution of training activation maps of the deep learning model, based on samples from the training dataset. In various cases, the system can generate a set of target activation maps of the deep learning model, by feeding a set of samples from the target dataset to the deep learning model.

Abstract: A system and method for enhancing visualization of a pleural line by automatically detecting and marking the pleural line in images of an ultrasound scan is provided. The method includes receiving an ultrasound cine loop acquired according to a first mode. The method includes processing the ultrasound cine loop according to the first mode. The method includes processing at least a portion of the ultrasound cine loop according to a second mode. The method includes identifying a position of an anatomical structure based on the at least a portion of the ultrasound cine loop processed according to the second mode. The method includes displaying, at a display system, the position of the anatomical structure on a first mode image generated from the ultrasound cine loop processed according to the first mode.

Abstract: Systems and methods for tissue specific time gain compensation of an ultrasound image are provided. The method comprises acquiring an ultrasound image of a subject and displaying the ultrasound image over a console. The method further comprises selecting by a user a region within the ultrasound image that requires time gain compensation. The method further comprises carrying out time gain compensation of the user selected region of the ultrasound image. The method further comprises identifying a region having a similar texture to the user selected region and carrying out time gain compensation of the user selected region by an artificial intelligence (AI) based deep learning module.