Exo Imaging, Inc.

Masood Dehghan

Dornoosh Zonoobi

Koosha Pourtahmasi Roshandeh

Jacob Jaremko

Abhilash Rakkunedeth

Roberto Ivan Vega Romero

Zichen Zhang

Xuebin Qin

Mahdiar Nekoui

Amir Safarpoor Kordbacheh

Amirhosein Forouzandehmoghadam

Seyed Ehsan Seyed Bolouri

A method includes generating a series of two-dimensional (2D) ultrasound images of the tissue volume associated with a plurality of positions, respectively. The method includes, for a respective pair of consecutive 2D ultrasound images of the series of 2D ultrasound images: generating a difference image from the respective pair of consecutive 2D ultrasound images; estimating, using a deep neural network, an estimated distance between the positions associated with the respective pair of consecutive 2D ultrasound images based on a classification of the difference image, wherein the deep neural network is trained to classify the difference image based on one of a plurality of predefined classes. The method includes rendering the 3D ultrasound image of the tissue volume based on the series of 2D ultrasound images and the estimated distance.

Method and system for generating a three-dimensional ultrasound image of a tissue volume from two-dimensional ultrasound images

A method includes obtaining a first frame of a probed region acquired using a first probe device at a first time, and a first set of control parameters used to acquire the first frame. Processing the first frame includes segmenting features from the first frame after first probe-specific effects are reduced in the first frame based on the first set of control parameters. The method includes, when the first frame contains a respective attribute present in a second frame acquired at a time earlier than the first time using a second set of control parameters, displaying information related to differences in the respective attribute in the first frame and the second frame. The second frame is obtained by processing the one or more features segmented from the second frame after one or more second probe-specific effects are reduced in the second frame.

Device agnostic systems and methods for acquiring and analyzing images from an ultrasound probe

A non-transitory computer readable storage medium has instructions executed by a processor to receive an ultrasound image. The ultrasound image is applied to a sequence of encoders where each encoder in the sequence of encoders performs convolution neural network processing of a down-sampled version of the ultrasound image from a prior encoder, the sequence of encoders form a first dimension. The ultrasound image is applied to a transition encoder with an orthogonal dimension to the first dimension. The ultrasound image is applied to a sequence of decoders where each decoder in the sequence of decoders performs convolution neural network processing of an up-sampled version of the ultrasound image from a prior decoder, the sequence of decoders form a second parallel dimension to the first dimension. Encoder and decoder configurations and the first dimension, the orthogonal dimension and the second parallel dimension thereby define a nested U network architecture. Probability segmentation maps are produced from paired encoders and decoders in the sequence of encoders and the sequence of decoders. The probability segmentation maps are combined to form a final probability segmentation output.

Apparatus and method for image segmentation using a deep convolutional neural network with a nested U-structure

A method may include generating a series of two-dimensional (2D) ultrasound images of the tissue volume associated with a plurality of positions along a scanning direction of the tissue volume; estimating, for each pair of consecutive 2D ultrasound images of the series of 2D ultrasound images, a distance between the positions associated with the pair of consecutive 2D ultrasound images based on a classification of a difference image generated from the pair of consecutive 2D ultrasound images using a deep neural network to produce a plurality of estimated distances associated with the plurality of pairs of consecutive 2D ultrasound images, respectively; modifying the number of 2D ultrasound images in the series of 2D ultrasound images based on the plurality of estimated distances to produce a modified series of 2D ultrasound images; and rendering the 3D ultrasound image of the tissue volume based on the modified series of 2D ultrasound images.

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Masood Dehghan