Disclosed are a medical image segmentation method and system based on generative adversarial network, and an electronic equipment. First, studying the way a generator extracts the pixel-level features of different types of high-quality images, and using a capsule model to perform structured feature representation for generating pixel-level annotation samples; secondly, building an appropriate discriminator for discriminating the authenticity of the generated pixel-level annotation samples, designing an appropriate error optimization function, and feeding the discrimination result back to the generator model and the discriminator model respectively so as to improve the sample generation ability and the discrimination ability of the generator and the discriminator respectively after continuous adversarial training; and finally, using the trained generator to generate pixel-level annotation samples for achieving pixel-level segmentation of a medical image with image-level annotation. The method effectively reduces the dependence of a segmentation model on pixel-level annotation data, thereby not only improving the efficiency of adversarial training of generated samples against real samples, but also effectively achieving high-precision pixel-level image segmentation.
MEDICAL IMAGE SEGMENTATION METHOD AND SYSTEM BASED ON GENERATIVE ADVERSARIAL NETWORK, AND ELECTRONIC EQUIPMENT