Image segmentation method and device, equipment and storage medium

Abstract

The invention discloses an image segmentation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a first image sample and a second image sample, wherein the quality of annotation data of the first image sample is higher than that of annotation data of the second image sample; performing segmentation processing on the first image sample and the second image sample through a student network and a teacher network to obtain a student segmentation result of the first image sample, a student segmentation result of the second image sample, a teacher segmentation result of the first image sample and a teacher segmentation result of the second image sample; determining training loss according to the annotation data of the first image sample and the segmentation result; and training the student network based on the training loss. The image information is extracted from the samples with the low-quality labels through the teacher network, and the student network is trained, so that the problem of model overfitting caused by lack of the samples with the high-quality labels is avoided.

Description

technical field [0001] The embodiments of the present application relate to the technical field of artificial intelligence, and in particular to an image segmentation method, device, equipment, and storage medium. Background technique [0002] With the development of artificial intelligence technology, developers try to achieve the task of segmenting blood vessels in organs through deep learning technology. [0003] Taking the liver vessel segmentation task as an example, related technologies train the image segmentation model based on liver images with high-quality vessel annotations, so as to obtain an image segmentation model that can be used to segment liver vessels. [0004] However, due to the general lack of liver images with high-quality blood vessel annotations, related technologies can only rely on a small number of liver images with high-quality blood vessel annotations to train the image segmentation model, resulting in over-fitting of the image segmentation mode...

AI Technical Summary

Problems solved by technology

[0004] However, due to the general lack of liver images with high-quality blood vessel annotations, related technologies can only rely on a small number of liver images with high-quality blood vessel annotations to train the image segmentation model, resulting in over-fitting of the image segmentation model and low accuracy of segmentation results

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