Blood vessel segmentation network and method based on generative adversarial network

Abstract

The invention discloses a blood vessel segmentation network and method based on a generative adversarial network. The segmentation network comprises a generation model and a discrimination model. Residual connection is added to the generation model on the basis of adopting a U-shaped encoding-decoding structure; the discrimination model adopts a full convolution form of a VGG network, and replacesthe convolution layer of the middle part with a dense connection module; according to the segmentation method, a generation sample of a color fundus image is generated through a generation model, thegeneration sample and a corresponding real sample are input into a discrimination model, alternate training optimization is performed on the generation model and the discrimination model, and finally, a to-be-segmented retinal vessel color image is input into the trained and optimized model, so that a vessel segmentation result can be output. According to the method, more tiny capillaries in theretinal vessel image can be detected, the vessel edge can be positioned more accurately, the vessel segmentation precision is greatly improved, and the vessel image segmentation sensitivity, effectiveness and stability are greatly improved.

Description

technical field [0001] The present invention relates to the technical field of convolutional neural network and retinal vessel segmentation, in particular to a vessel segmentation network and method based on a generative confrontation network. Background technique [0002] Eyes are one of the most important sensory organs in the human body, yet many people around the world suffer from blindness. Among many blinding eye diseases, fundus diseases such as age-related macular degeneration, diabetic retinopathy, and hypertensive retinopathy are the main causes of blindness. From a clinical point of view, the existence of these diseases is closely related to the morphological changes of the diameter, curvature, branch form or angle of retinal blood vessels. Ophthalmologists can understand the blood vessels of other organs by observing the segmented fundus retinal blood vessel images. Diagnosis of eye diseases and various diseases of the whole body. However, in actual clinical di...

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Problems solved by technology

[0011] Although the above methods using GAN have obtained relatively good segmentation results, there is still a problem of insufficient segmentation accuracy for the segmentation of low-pixel capillaries, because the characteristic of adversarial training is the confrontation between the two models, and the improvement of the performance of one side is also a problem. Suppressing the performance of the other party, the lack of discriminative ability of the discriminant model will be confused by the new samples generated by the generative model, unable to correctly distinguish between real samples and generated samples, and the accuracy of segmentation cannot be guaranteed

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