Eye fundus image blood vessel segmentation method and system based on self-supervised learning

Abstract

The invention discloses an eye fundus image blood vessel segmentation method and system based on self-supervised learning. The method comprises the following steps: improving a U-net structure by a used network model, mutually transmitting different layers of feature maps to meet requirements of eye fundus image detail feature extraction, and increasing the speed of eye fundus image blood vessel segmentation in a segmentation process through network pruning; then designing and adopting an aggregation task strategy, and combining four methods of intensity transformation, random pixel filling, inward filling and outward filling so as to obtain more global features and detail features of an eye fundus image in a pre-training learning process; finally, designing a vector classification task module to generate different vector routes, and training an encoder through a network prediction vector route to obtain spatial correlation features of the eye fundus image. According to the method, effective eye fundus image features can be learned from unlabeled data, and blood vessel segmentation precision equivalent to that of a supervised deep learning method can be achieved with fewer training iterations and manual labeled data.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to a fundus image blood vessel segmentation method and system, in particular to a fundus image blood vessel segmentation method and system based on self-supervised learning. Background technique [0002] With the further improvement of the material and spiritual quality of people in society, the per capita old age rate in our country will also further increase. However, the mortality rate of patients with age-related diseases and metabolic-related diseases has risen rapidly, and fundus diseases are becoming a new challenge that threatens the health and vision of urban residents in my country. The method of fundus image analysis can enable people to discover various eye diseases as early as possible in medicine, so as to facilitate timely treatment of patients. Retinal vessel segmentation in fundus images is of great significance to the prediction, diagnosis and subsequent tre...

AI Technical Summary

Problems solved by technology

However, the existing fundus image blood vessel segmentation algorithm needs to use a large amount of annotation data, and the quality requirements for the annotation data of fundus image segmentation are high.

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