An eye fundus image cup-disc segmentation method based on generative adversarial mechanism

Abstract

The invention discloses an eye fundus image cup-disc segmentation method based on a generative adversarial mechanism. The method includes the steps that data enhancement is carried out on a single-channel or multi-channel color eye fundus image, the fundus image is segmented through a U-Net network, the predictive segmentation image will be sent to the discriminator network to identify the true and false, the true and false judgment loss returns back to adjust a model generated by the U-Net network, after many times of running of a generative adversarial network, and finally the optimal opticdisc segmentation model and optic cup segmentation model are obtained. The method achieves the optic disc and optic cup segmentation detection.

Description

technical field [0001] The invention relates to the field of ocular medical image detection, and more specifically, to a cup-and-disc segmentation method for fundus images based on a generative confrontation mechanism. Background technique [0002] Glaucoma is a chronic eye disease in which the optic nerve is gradually damaged, and it is the second leading cause of blindness after cataract. The cup-to-disk ratio is the most commonly used detection method for glaucoma at home and abroad. Pressure and other measurement methods, the calculation of cup-to-disk ratio is relatively stable, and the carriers used for cup-to-disk ratio detection are generally fundus images such as fundus maps and OCT, which can be saved and used for sorting and analysis of medical big data. [0003] Use computer technology to analyze and process digital images to detect and segment the optic disc and cup of the fundus image, and the results are used as auxiliary opinions for diagnostic physicians to ...

AI Technical Summary

Problems solved by technology

However, the segmentation model training time of existing machine learning and deep learning methods is long

[0007] To sum up, the existing computer-aided technology for optic disc and optic cup segmentation still cannot meet the requirements of high accuracy and low computing time for glaucoma detection at the same time, nor can it realize the segmentation of optic disc and optic cup on fundus images of different imaging technologies. segmentation

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