Retinal vessel segmentation method in fundus image and computer readable storage medium

Abstract

The invention provides a retinal vessel segmentation method in a fundus image and a computer readable storage medium. The method comprises the steps of preprocessing the fundus image of a public dataset; cutting and expanding the preprocessed fundus image and dividing the fundus image into a training set and a cross validation set; building a residual non-local attention network, wherein the residual non-local attention network comprises the steps of adopting a coding network decoding network architecture with jump connection as a basic model, and embedding a residual non-local attention module to capture a non-local context dependency relationship between different areas of input features; adding a pyramid pooling module to the encoding network to capture multi-scale features; training aresidual non-local attention network by adopting the training set; determining model parameters of the residual non-local attention network through the cross validation set; and adopting the trainedresidual non-local attention network to segment the fundus image to be processed, so that a retinal vessel segmentation image can be obtained. Segmentation performance is better.

Description

Technical field[0001]The present invention relates to the technical field of medical image processing, in particular to a method for segmenting retinal blood vessels in fundus images and a computer-readable storage medium.Background technique[0002]The analysis of retinal blood vessels can provide rich information about eye conditions and human health. Among them, obtaining a clear blood vessel distribution map is a crucial step.[0003]Recently, retinal blood vessel segmentation methods often use Convolutional Neural Network (CNN) as the basic model algorithm, and this model algorithm has shown excellent feature extraction capabilities in segmentation tasks. However, the fixed receptive field prevents the network from adapting to changes in the scale of retinal blood vessels. Because the retinal vessel structure is complex, entangled and multi-scale. In addition, the local receptive field cannot capture the global information and context dependence between different regions of the ent...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the existing problems, the present invention provides a method for segmenting retinal blood vessels in fundus images and a computer-readable storage medium

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