Retinal blood vessel segmentation method and device, electronic equipment and storage medium

Abstract

The invention provides a retinal blood vessel segmentation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring eye fundus images, dividing the eye fundus images into a training set and a test set, and performing corresponding preprocessing operation on the eye fundus images in the training set and the test set; respectively constructing a segmentation network and a discrimination network; inputting the marked fundus images in the training set into a segmentation network for training, inputting the unmarked fundus images in the training set into the segmentation network after training for a preset round, and alternately training the segmentation network and the discrimination network to obtain a trained retinal vessel segmentation model; inputting a fundus image to be segmented into the retinal vessel segmentation model to obtain a segmented output image; and splicing all the output images to obtain a retinal blood vessel segmentation result image. The retinal blood vessel in the fundus image can be automatically and accurately extracted, the segmentation result comprises tiny details of the blood vessel, and detail information of the image is richer and can be used for clinical auxiliary diagnosis.

Description

technical field [0001] The disclosure belongs to the technical field of medical image processing, and in particular relates to a retinal blood vessel segmentation method and device, electronic equipment, and a storage medium. Background technique [0002] Retinal blood vessels are an important part of the eyeball, and many characteristics such as the shape of the vascular network can directly reflect some diseases. Especially chronic diseases such as diabetes and high blood pressure, and ophthalmic diseases such as various retinal vascular diseases are examples. Hemorrhage, edema, hardening, exudation, and hemangioma-like changes of fundus blood vessels reflect some known or unknown lesions of the human body and their characteristics and degrees. At the same time, the treatment of retinal diseases (such as retinal photocoagulation) needs to avoid normal retinal blood vessels. Accurately segmenting blood vessels is the basic technology of the fully intelligent retinal laser ...

AI Technical Summary

Problems solved by technology

However, in practical applications, since annotating medical images, especially retinal vessels with complex topological structures, requires a lot of time for experienced clinical experts, the labeled data is often very scarce, leading to retinal vessel segmentation methods based on fully supervised machine learning. Difficult to effectively apply in actual clinical practice

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