Cup-to-disk ratio determination method and device based on neural network, equipment and storage medium

Abstract

The invention relates to the field of artificial intelligence, specifically uses a neural network, and discloses a cup-to-disk ratio determination method, device and equipment based on the neural network, and a storage medium. The method comprises the steps: obtaining an eye fundus image, and carrying out the optic disk region detection of the eye fundus image, so as to obtain an optic disk region; inputting the optic disk area into a pre-trained neural network to obtain a predicted cup-to-disk ratio and a segmented image of the optic cup and the optic disk; determining an arithmetic cup-to-disk ratio based on the segmented image of the optic cup and the optic disk; and determining an actual cup-to-disk ratio according to the arithmetic cup-to-disk ratio and the predicted cup-to-disk ratio. The accuracy of the determined cup-to-disk ratio is improved, and the situations of multiple screening and screening omission in the disease screening process are reduced. The invention is applicable to the field of smart medical treatment.

Description

technical field [0001] The present application relates to the field of image processing, and in particular to a neural network-based cup-to-plate ratio determination method, device, equipment and storage medium. Background technique [0002] Glaucoma is one of the three major eye diseases that cause blindness in the world. Its irreversibility makes its early diagnosis and treatment play a vital role in improving the quality of life of patients. In the automatic screening of glaucoma, the cup-to-disk ratio is usually used as an evaluation index, and the cup and disc in the fundus image are segmented by a segmentation method, and then the cup-to-disk ratio is calculated. However, the existing optic disc segmentation methods are easily affected by the image quality of the fundus image, such as illumination, occlusion, etc., resulting in a decrease in segmentation accuracy, which in turn leads to a low accuracy of the calculated cup-to-disc ratio, and is prone to multiple or mis...

AI Technical Summary

Problems solved by technology

However, the existing optic disc segmentation methods are easily affected by the image quality of the fundus image, such as illumination, occlusion, etc., resulting in a decrease in segmentation accuracy, which in turn leads to a low accuracy of the calculated cup-to-disc ratio, and is prone to multiple or missing sieves. Happening

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