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:
• generate a series of two-dimensional (2D) ultrasound images of the tissue volume associated with a plurality of positions, respectively, along a scanning direction of the tissue volume based on a series of ultrasound waves acquired by the ultrasound transducer at the plurality of positions;
• estimate, 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;
• modify the number of 2D ultrasound images in the series of 2D ultrasound images based on the plurality of estimated distances to produce a modified set of 2D ultrasound images;
• and
• render the 3D ultrasound image of the tissue volume based on 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 the tissue volume associated with a plurality of positions, respectively, 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 set of 2D ultrasound images;
• and
• rendering the 3D ultrasound image of the tissue volume based on the modified series of 2D ultrasound images.