U-Net fundus retinal blood vessel image segmentation method and device based on improvement

Abstract

The invention provides a U-Net fundus retinal blood vessel image segmentation method based on improvement. The method comprises the following steps: acquiring a to-be-detected retinal blood vessel image; inputting the retinal blood vessel image into a trained improved U-Net network model for image segmentation; and outputting a retinal blood vessel image segmentation result. A common convolution block of the U-Net network is changed into a random discarded convolution block, so that image related features can be better extracted, and network over-fitting is effectively relieved; then, a NoL-block attention module is added to the bottom of a coding and decoding structure, and on the basis of not increasing the parameter quantity, the receptive field is expanded, and the correlation of pixel information is enhanced; the NoL-block is used as a component and is embedded into the U-Net network structure, so that the NoL-block can be used for directly capturing the spatial dependency relationship between pixels with distances in image segmentation in a forward propagation manner, and the blood vessel can be better positioned and segmented.

Description

technical field [0001] The invention belongs to the field of image segmentation, and in particular relates to an improved U-Net-based fundus retinal blood vessel image segmentation method and device. Background technique [0002] Segmentation of retinal vessels is important for the diagnosis and screening of retinal vascular diseases, including diabetes, ophthalmic diseases, and cardiovascular diseases. With the increasing number of visually impaired patients and the shortage of medical resources, especially the serious shortage of qualified ophthalmologists, ophthalmology patients often suffer from irreversible vision damage due to missing the best treatment period. The precise segmentation of fundus blood vessels can effectively assist ophthalmologists in their diagnosis and improve their work efficiency. [0003] With the continuous development of image processing technology, existing methods can be divided into unsupervised methods and supervised methods according to wh...

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Problems solved by technology

[0005] However, the accuracy of the existing methods for the segmentation of fine-grained blood vessel regions is still relatively low, especially for the low-contrast info

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