A method and device for automatically measuring retinal artery-to-vein diameter ratio

Abstract

The invention discloses a retinal artery vein diameter ratio automatic measuring method and a device. The retinal artery vein diameter ratio automatic measuring device can automatically calculate thecorresponding AVR indexes, increase the accuracy and automation degree of AVR calculation, realize accurate measurement of the AVR indexes, and accelerate clinical disease diagnosis and treatment speed. The method comprises the following steps: (1) acquiring a retina image by using fundus imaging equipment; (2) carrying out optic disc positioning and blood vessel segmentation; (3) based on the center line, carrying out blood vessel topology analysis; and (4) based on a deep convolutional network, classifying the blood vessel tree into arteries or veins, calculating the diameter and category ofsampling point positions, and completing the measurement of the retinal artery-vein diameter ratio AVR.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to an automatic measurement method for retinal artery-to-venous diameter ratio (AVR) and an automatic measurement device for retinal artery-to-venous diameter ratio. Background technique [0002] Under the influence of certain systemic diseases such as diabetes and hypertension, the pressure within the retinal blood vessels changes abnormally, resulting in changes in the shape of the blood vessels. AVR (Arteriolar-to-Venular Diameter Ratio) is an important clinical indicator for evaluating changes in intravascular pressure levels, and is highly correlated with the five-year incidence of cardiovascular disease. In addition, AVR can predict the occurrence of stroke in middle-aged people, and provide auxiliary diagnostic information for retinopathy of prematurity syndrome and diabetic retinopathy. Therefore, designing an accurate and automatic AVR measurement ...

AI Technical Summary

Problems solved by technology

[0004] 1. The blood vessel area needs to be extracted first for AVR measurement, but the retinal image blood vessel imaging is not clear, and the blood vessel scale changes too much, resulting in incomplete blood vessel area

[0005] 2. AVR measurement needs to calculate the diameters of arteries and veins separately, but the graphics are not balanced in light, and there are a lot of overlaps between arteries and veins. The current classification algorithm is difficult to meet the needs of accurate classification

[0006] 3. The existing AVR measurement algorithm needs to rely on a certain amount of manual interaction to realize the segmentation and classification of blood vessels, which reduces the processing speed of the system

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