MR image segmentation method and device based on conditional generative adversarial network

Abstract

The invention discloses an MR image segmentation method and device based on a conditional generative adversarial network, and the method comprises the steps: training a cGAN network through a low-level MR image y and a segmentation mask x, and obtaining a trained cGAN model; automatically generating an artificial image y' by using the trained cGAN model according to the input segmentation mask x;pre-training a U-net network by utilizing the artificial image y' and the segmentation mask x; continuously training the U-net network through the low-level MR image y and the segmentation mask x to obtain a trained U-net model; and performing MR image segmentation by using the trained U-net model. The invention also provides a device for realizing the method, terminal equipment and a computer readable storage medium, the data diversity of an original data set is expanded and enriched, an image segmentation result is closer to a real image, and the convergence speed, the loss and the segmentation precision in a segmentation network training process are higher.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an MR image segmentation method and device based on a conditional generation confrontation network. Background technique [0002] In recent years, image segmentation technology has been more and more applied in the field of medical imaging. For example, diffuse low-grade glioma (Lower-Grade Gliomas, LGG) is the most common central nervous system tumor. LGG is a grade II and III glioma defined by the World Health Organization (WHO), including astrocytoma, oligodendroglioma, and oligoastrocytoma. Due to the highly aggressive nature of grade II and III gliomas, complete surgical resection is impossible, and residual tumor after resection usually leads to recurrence and progression to glioblastoma (Glioblastoma, GBM) . It is well known that the earlier a tumor is detected, the higher the patient's chances of survival. In the prior art, Magnetic Resonance Imaging (MRI) is usually use...

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide a kind of MR image segmentation method and device based on conditional generative confrontation network for the problem of insufficient image segmentation and labeling data in the above-mentioned prior art, Make artificially generated image segmentation results closer to real images

Images