The invention provides a
fundus image blood vessel segmentation method which comprises the following steps of S1, carrying out augmented operation on a
fundus image data set, namely
cutting an original image according to a certain rule to obtain an augmented
data set, and dividing the
data set into a
training set and a
test set; S2, carrying out
blood vessel segmentation on each
fundus image sample in the augmented training data set by using a segmentation network to obtain a black-white probability graph, and carrying out
image processing to obtain a probability graph of segmented images in the training data set; S3, using a discrimination network to distinguish whether the probability graph is obtained by a segmentation network or a segmentation image in a data set, and obtaining a confidence graph with a numerical value range of 0-1 to describe the probability that the input image is judged to be true or false; and S4, inputting the color fundus image in the
test set into the trained network, and splicing the obtained result images to obtain a complete segmentation image of the fundus
blood vessel, wherein the step S2 and the step S3 perform fundus image blood
vessel segmentation based on the
generative adversarial network, and before the step S4, the segmented network in the step S2 and the judgment network in the step S3 are sequentially subjected to iterative training according to the designed
loss function, and the performances of the two networks are respectively improved in a game mode. The method is based on the superiority of the generative adversarial idea in data generation, does not depend on selection of initial features, reduces the
complex calculation steps, effectively improves the segmentation precision of fundus image blood vessels, and especially has the excellent performance on tiny blood vessels.