Image segmentation method and apparatus, computer device, and storage medium

An image segmentation apparatus, comprising:

• a memory;
• and a processor coupled to the memory, the processor being configured to perform:
• pre-training a first initial model based on a plurality of first sample images to obtain a second initial model, the plurality of first sample images including images of a plurality of human tissues, and the second initial model including distribution information of a plurality of target regions corresponding to the plurality of human tissues;
• training the second initial model based on a plurality of second sample images to obtain an image segmentation model, the plurality of second sample images including images of a target human tissue, the image segmentation model obtaining image information of the plurality of second sample images in a training process, and the image information of the plurality of second sample images including at least distribution information of a plurality of target regions corresponding to the target human tissue;
• and
• feeding a first image to the image segmentation model, and segmenting, by using the image segmentation model, the first image according to the image information to output a second image, wherein one of the first initial model, the second initial model, and the image segmentation model includes a first image segmentation module and a second image segmentation module, the first image segmentation module and the second image segmentation module each correspond to a segmentation algorithm, including a gradient descent algorithm or an online hard example mining algorithm, the first image segmentation module is configured to segment a three-dimensional (3D) image, and the second image segmentation module is configured to segment a two-dimensional (2D) image.

A non-transitory electronic-readable storage medium storing computer program instructions executable by at least one processor to perform:

• pre-training a first initial model based on a plurality of first sample images to obtain a second initial model, the plurality of first sample images including images of a plurality of human tissues, and the second initial model including distribution information of a plurality of target regions corresponding to the plurality of human tissues;
• training the second initial model based on a plurality of second sample images to obtain an image segmentation model, the plurality of second sample images including images of a target human tissue, the image segmentation model obtaining image information of the plurality of second sample images in a training process, and the image information of the plurality of second sample images including at least distribution information of a plurality of target regions corresponding to the target human tissue;
• and
• feeding a first image to the image segmentation model, and segmenting, by using the image segmentation model, the first image according to the image information to output a second image, wherein one of the first initial model, the second initial model, and the image segmentation model includes a first image segmentation module and a second image segmentation module, the first image segmentation module and the second image segmentation module each correspond to a segmentation algorithm, including a gradient descent algorithm or an online hard example mining algorithm, the first image segmentation module is configured to segment a three-dimensional (3D) image, and the second image segmentation module is configured to segment a two-dimensional (2D) image.