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

A system for generating a three-dimensional (3D) ultrasound image of a tissue volume, the system comprising:

• an ultrasound transducer;
• a memory;
• and
• at least one processor communicatively coupled to the memory and the ultrasound transducer, and configured to perform a set of operations, comprising:
• 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;
• for a pair of consecutive 2D ultrasound images of the series of 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, wherein the deep neural network is trained to classify differences between images based on one of a plurality of predefined classes;
• 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;
• and
• performing a measurement of tissue using at least one of the modified series of 2D ultrasound images.

A computer program product, embodied in one or more non-transitory computer-readable storage mediums, comprising instructions executable by at least one processor to perform a method for generating a three-dimensional (3D) ultrasound image of a tissue volume, the method comprising:

• 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;
• for a pair of consecutive 2D ultrasound images of the series of 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, wherein the deep neural network is trained to classify differences between images based on one of a plurality of predefined classes;
• 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;
• and
• performing a measurement of tissue using at least one of the modified series of 2D ultrasound images.