Medical image segmentation method and system based on distributed generative adversarial network

Abstract

The invention discloses a medical image segmentation method and system based on a distributed generative adversarial network, and the method comprises the steps of setting discriminators at all hospitals, setting a generator on a central server, and constructing the generative adversarial network between each discriminator and the generator; acquiring a medical image of each hospital; training the generative adversarial network through the medical image of each hospital; and segmenting the medical image to be segmented through the trained generative adversarial network. The generative adversarial network is trained through the medical images of the hospitals, the data set during network training is expanded, and the network training effect is improved.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a medical image segmentation method and system based on a distributed generative confrontation network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In the field of medical imaging, accurate medical images play a very important auxiliary role in many clinical applications. In clinical practice, multimodal medical images have been widely used. However, manually segmenting medical images of all modalities is time-consuming and labor-intensive, and there are discrepancies between the segmentation results of different physicians. In order to reduce the workload and establish a unified segmentation standard, computer automatic segmentation is particularly important. [0004] When existing hospitals segment medical images, they...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems, this disclosure proposes a medical image segmentation method and system based on a distributed generative confrontation network, constructs a generative confrontation network between each hospital and the central server, obtains medical images of each hospital to train the generative confrontation network, and solves the problem It solves the technical problem of few training samples when training the existing model, improves the training accuracy of the generative confrontation network model, and further improves the accuracy of medical image segmentation

Images