Abstract: 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.

Abstract: A method includes generating a series of two-dimensional (2D) ultrasound images of a tissue volume associated with a plurality of positions, respectively, along a scanning direction of the tissue volume. The method includes, for a pair of consecutive 2D ultrasound images, using a deep neural network to classify a difference between the pair of consecutive 2D ultrasound images of the series of 2D ultrasound images. The deep neural network is trained to classify differences between images based on one of a plurality of predefined classes. The method includes modifying a number of 2D ultrasound images in the series of 2D ultrasound images based on the classification of the difference between the pair of consecutive 2D ultrasound images of the series of 2D ultrasound images, thereby producing a modified series of 2D ultrasound images. The method includes performing a measurement of tissue using the modified series of 2D ultrasound images.

Abstract: 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.

Abstract: 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.

Abstract: 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.