Carnegie Mellon University

University of Pittsburgh

John Galeotti

Tejas Sudharshan Mathai

Howie Choset

Jiayuan Li

Ricardo Luis Rodriguez

Vijay Saradhi Gorantla

Gautam Rajendrakumar Gare

Edward Chen

Provided is a system, method, and computer program product for analyzing spatio-temporal medical images using an artificial neural network. The method includes capturing a series of medical images of a patient, the series of medical images comprising visual movement of at least one entity, tracking time-varying spatial data associated with the at least one entity based on the visual movement, generating spatio-temporal data by correlating the time-varying spatial data with the series of medical images, and analyzing the series of medical images based on an artificial neural network comprising a plurality of layers, one or more layers of the plurality of layers each combining features from at least three different scales, at least one layer of the plurality of layers of the artificial neural network configured to learn spatio-temporal relationships based on the spatio-temporal data.

System and method for analyzing medical images based on spatio-temporal data

Provided is a method of training a machine-learning-based artificial intelligence (AI) model to handle diverse types of motions occurring during image acquisition, including capturing image data including motion between an imaging device and tissue, modifying the captured image data, resulting in modified image data, by at least one of: altering an amount of time between any two frames; removing a subsequence of frames from the captured image data; and adding a subsequence of one or more new frames to the captured image data, and training a machine-learning-based AI model based on the modified image data. Other systems and methods are also described.

System and method for domain generalization across variations in medical images

Provided are methods for labeling ultrasound data. The method may include training a convolutional neural network (CNN) based on ultrasound data. The ultrasound data may include ultrasonic waveform data (e.g., radio frequency (RF) waveform data). An RF input of each downsampling layer of a plurality of downsampling layers in the CNN may be downsampled. The RF input may include RF waveform data for an ultrasound. Tissues in the ultrasound may be segmented based on an output of the CNN. A system is also disclosed.

System and method for labeling ultrasound data

Provided is a system, method, and computer program product for segmenting vessels in an ultrasound image. The method includes detecting edges of a vessel in the ultrasound image; detecting a vessel contour of the vessel in the ultrasound image based on the detected edges and a distance regularized level set evolution; and tracking the vessel contour with a Kalman Filter.

System and method for vessel segmentation

Provided is a system, method, and computer program product for creating a deep-learning model for processing image data. The method includes establishing dense connections between each layer of a plurality of layers of a convolutional neural network (CNN) and a plurality of preceding layers of the CNN, downsampling an input of each downsampling layer of a plurality of downsampling layers in a first branch of the CNN, and upsampling an input of each upsampling layer of a plurality of upsampling layers in a second branch of the CNN by convolving the input.

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Pittsburgh, PA, United States of America

John Galeotti