Fundus retina image segmentation method based on generative adversarial network

Abstract

An existing fundus retina image segmentation technology has defects in retina vessel segmentation precision, and a good method for segmenting small capillaries does not exist. In order to realize accurate segmentation of a fundus retina image, the invention provides a fundus retina image segmentation method based on a generative adversarial network (C-GAN). The method comprises: a generation modelextracting abstract features through a coding-decoding network structure and carrying out detail recovery to obtain a blood vessel segmentation probability graph close to a sample true value; the discrimination network inputting the sample probability graph obtained by the generation model and a sample true value label into a deep convolutional network, giving a higher label to a real sample, andgiving a lower label to a generated sample. The network parameter weight is adjusted through mutual counterbalance of the generation model and the discrimination model, and the network converges thewhole network when the generation model and the discrimination model reach balance. According to the invention, tiny capillaries of the fundus image can be segmented, and the segmentation precision ofthe whole retinal vessel is improved.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a method for segmenting retinal vessels in fundus images. Background technique [0002] The fundus is the only microcirculatory part of the human body where the blood circulation can be directly seen. Currently common fundus diseases that affect visual function such as diabetic retinopathy, age-related macular degeneration, retinal vein occlusion and other diseases are related to the diameter, curvature and branching form of retinal blood vessels. Or the morphological change of the angle is closely related. In ophthalmology, fundus examination is very important. Ophthalmologists observe the fundus retinal blood vessels as a window to understand the blood vessels of other organs. Many diseases can be reflected from the fundus. Therefore, the fundus is an important basis for diagnosing eye diseases and various diseases in the whole body. And early reti...

AI Technical Summary

Problems solved by technology

It is necessary to design the segmentation model based on the existing prior knowledge and complex probability and statistics methods, and it is necessary to assume that the training data and the test data obey the same distribution, which will cause the training results to be better than the test results, that is, the problem of overfitting , causing the segmentation results to be blurred, and the segmentation results need to be binarized after processing, which has certain limitations in the segmentation of retinal capillaries

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