Fundus image retinal vessel segmentation method based on evolutionary neural architecture search

Abstract

According to a fundus image retinal vessel segmentation method based on evolutionary neural architecture search provided by the invention, a U-shaped decoding and coding structure is used as a backbone network to search and optimize the internal structures of modules in the backbone network, so that structures and operations which are better than those of artificial design and have lower computational complexity are found for the modules; according to the invention, the neural network model can more effectively process the complex condition of the fundus image, has higher robustness for interference in the complex fundus image focus, the vascular center reflection phenomenon and the illumination imbalance phenomenon, and can more accurately extract the features of retinal vessels, therebyimproving the segmentation accuracy of the whole image. According to the method, the flexibility of the architecture is guaranteed, the searching efficiency of the architecture is improved, the crossoperation in the genetic algorithm is improved, the searching capability of the genetic algorithm in the architecture searching process is improved, and the method has more potential to be applied toclinical disease diagnosis.

Description

Technical field[0001]The invention belongs to the field of computer technology and image processing technology, and specifically relates to a method for segmenting retinal blood vessels in fundus images based on an evolutionary neural architecture search.Background technique[0002]Retinal fundus image analysis is widely used in the diagnosis, screening and clinical research of ophthalmic diseases such as glaucoma and cataracts, diabetes, hypertension and arteriosclerosis and other cardiovascular diseases. The precise segmentation of retinal blood vessels is the most important step in retinal fundus image analysis. Retinal blood vessels can not only reflect the condition of diseases such as diabetic retinopathy, but also help locate and locate retinal fundus lesions such as microaneurysms and hard exudates. diagnosis. However, in clinical practice, retinal blood vessel segmentation is generally marked by ophthalmologists or experts, which is a tedious and time-consuming task that requ...

AI Technical Summary

Problems solved by technology

[0021] The purpose of the present invention is to propose a retinal blood vessel segmentation method for fundus images based on evolutionary neural architecture search, to solve one or more technical problems in the prior art, and at least provide a beneficial choice or create conditions

[0022] The retinal vessel segmentation method of the fundus image based on the evolutionary neural architecture search proposed by the present invention is used to solve the cumbersome work of artificially designing the retinal vessel segmentation neural network model, the workload is large, and the designed model is relatively complicated and the retinal vessel segmentation method in the complex fundus image is complicated. The problem of inaccurate blood vessel segmentation

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