Medical image segmentation method and system based on generative adversarial network, and electronic equipment

Abstract

The invention relates to a medical image segmentation method and system based on a generative adversarial network, and electronic equipment. The method comprises the following steps: firstly, researching how to extract pixel-level features of different types of high-quality images by a generator, and carrying out structured feature representation by utilizing a capsule model so as to generate a pixel-level labeled sample; secondly, constructing a proper discriminator for discriminating the authenticity of a generated pixel-level labeled sample, and designing a proper error optimization function; feeding back a discriminating result to models of a generator and a discriminator respectively, improving the sample generating capacity and the discriminating capacity of the generator and the discriminator respectively through continuous adversarial training, finally, adopting a trained generator to generate a pixel-level labeled sample, and achieving pixel-level segmentation of an image-level labeled medical image. According to the invention, the dependence of the segmentation model on pixel-level annotation data is effectively reduced, the adversarial training efficiency of the generated sample and the real sample can be improved, and high-precision pixel-level image segmentation can be effectively realized.

Description

technical field [0001] The present application belongs to the technical field of medical image processing, and in particular relates to a medical image segmentation method, system and electronic equipment based on generative adversarial networks. Background technique [0002] With the vigorous development of medical imaging technology, medical imaging has been widely and deeply applied in clinical medicine. According to statistics, tens of millions of cases are diagnosed and treated through medical imaging every year around the world. In the traditional method of diagnosis and treatment based on medical images, doctors read and recognize medical image data, and make judgments on the diagnosis and treatment of diseases. This method of diagnosis and treatment is very inefficient, and there are large individual differences. Doctors can easily miss and misdiagnose based on their personal experience. Long-term reading of films will lead to fatigue of doctors and a decrease in th...

AI Technical Summary

Problems solved by technology

The existing image segmentation model based on generative adversarial networks can be applied to cross-category object image segmentation, but in the field of medical images, the model has problems such as insufficient feature extraction and a large amount of calculation for adversarial training.

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