Volume acquisition method for object in ultrasonic image and related ultrasonic system

An object volume acquisition method of an ultrasonic image, for a probe of an ultrasonic system, wherein the volume acquisition method of the object in the ultrasonic image comprising:

• collecting, by the probe, a plurality of two-dimensional ultrasonic images;
• obtaining the plurality of two-dimensional ultrasonic images, an offset angle, a rotation axis and a frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images;
• segmenting a first image including an ultrasonic image object from each two-dimensional ultrasonic image of the plurality of two-dimensional ultrasonic images based on a deep learning structure;
• determining a contour of the ultrasonic image object according to the first image corresponding to the each two-dimensional ultrasonic image;
• reconstructing a three-dimensional model corresponding to the ultrasonic image object according to the contour of the ultrasonic image object corresponding to the each two-dimensional ultrasonic image;
• and
• calculating a volume of the ultrasonic image object according to the three-dimensional model corresponding to the ultrasonic image object;
• wherein the deep learning structure is a U-Net network structure for determining a preliminary contour and a location of the ultrasonic image object of the first image according to the offset angle, the rotation axis and the frequency of the probe corresponding to the plurality of two-dimensional ultrasonic images;
• wherein the step of determining the contour of the ultrasonic image object according to the first image corresponding to the each two-dimensional ultrasonic image includes:
• setting an edge threshold;
• detecting an edge of the first image;
• and
• generating a circle inside the preliminary contour of the ultrasonic image object based on the preliminary contour and the location of the ultrasonic image object of the first image, and expanding the circle outwardly till the circle reaches the edge threshold of the first image.