Patent ID: 11861501
Assignee: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A semantic segmentation apparatus for a three-dimensional image, the apparatus comprising: at least one processor and at least one memory, the at least one memory storing computer-readable instructions, wherein the at least one processor, when executing the computer-readable instructions, is configured to:
obtain a three-dimensional image;
slice the three-dimensional image according to three directional planes in which three-dimensional coordinate axes are located, to obtain two-dimensional slice images of an x axis, two-dimensional slice images of a y axis, and two-dimensional slice images of a z axis;
invoke a first segmentation model to perform semantic segmentation on the two-dimensional slice images of the x axis, to obtain a distribution probability map of a target object on an x-axis directional plane,
invoke a second segmentation model to perform semantic segmentation on the two-dimensional slice images of they axis, to obtain a distribution probability map of the target object on a y-axis directional plane;
invoke a third segmentation model to perform semantic segmentation on the two-dimensional slice images of the z axis, to obtain a distribution probability map of the target object on a z-axis directional plane;
invoke an adaptive fusion model to combine the three distribution probability maps respectively corresponding to the x-axis directional plane, the y-axis directional plane, and the z-axis directional plane, to obtain a three-dimensional distribution feature map;
perform three-dimensional fusion convolution on the three-dimensional distribution feature map, to obtain a three-dimensional segmentation probability map;
obtain a three-dimensional distribution binary image of the target object through calculation according to maximum probability categories of pixels in the three-dimensional segmentation probability map; and
filter out noise pixels in the three-dimensional distribution binary image based on prior knowledge, the prior knowledge being obtained by collecting statistics on a distribution location of the target object in sample three-dimensional images.