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CN-122023269-A - Evaluation method and device for hypertensive retinopathy grade

CN122023269ACN 122023269 ACN122023269 ACN 122023269ACN-122023269-A

Abstract

The invention provides a method and a device for evaluating the grade of hypertensive retinopathy, wherein one specific embodiment of the method comprises the steps of detecting the characteristics of the hypertensive retinopathy of fundus images to be detected of a target object; if the detection result represents that the feature of the hypertensive retinopathy exists in the fundus image to be detected, determining the risk level of the hypertensive retinopathy of the target object based on the feature of the hypertensive retinopathy. The embodiment can detect the characteristics of the hypertensive retinopathy in the fundus image based on the image processing technology of computer vision and deep learning so as to evaluate the risk level of the hypertensive retinopathy, and can effectively acquire the progress condition of the hypertensive retinopathy so as to better assist doctors in diagnosing the hypertensive retinopathy, thereby reducing the burden of the doctors, reasonably utilizing medical resources and improving the overall medical efficiency.

Inventors

  • DONG ZHOU
  • Ling Saiguang
  • KE XIN

Assignees

  • 依未科技(北京)有限公司

Dates

Publication Date
20260512
Application Date
20251231

Claims (10)

  1. 1. A method for evaluating the grade of hypertensive retinopathy is characterized in that, Carrying out feature detection on the high blood pressure retinopathy on the fundus image to be detected of the target object; If the detection result represents that the feature of the hypertensive retinopathy exists in the fundus image to be detected, determining the risk level of the hypertensive retinopathy of the target object based on the feature of the hypertensive retinopathy.
  2. 2. The method of claim 1, wherein the step of determining the position of the substrate comprises, Performing first hypertension retinopathy feature detection on a fundus image to be detected of a target object; If the first hypertensive retinopathy feature detection result represents that the first hypertensive retinopathy feature exists in the fundus image to be detected, determining the hypertensive retinopathy risk level of the target object based on the first hypertensive retinopathy feature; if the first hypertensive retinopathy feature detection result indicates that the first hypertensive retinopathy feature does not exist in the fundus image to be detected, performing artery and vein segmentation processing on the fundus image to be detected to generate an artery and vein segmentation result; Performing second hypertension retinopathy feature detection on the artery and vein segmentation result; And if the second hypertensive retinopathy feature detection result represents that the second hypertensive retinopathy feature exists in the fundus image to be detected, determining the hypertensive retinopathy risk level of the target object based on the second hypertensive retinopathy feature.
  3. 3. The method of claim 2, wherein the step of determining the position of the substrate comprises, Detecting whether the characteristic of optic disc edema exists in the fundus image to be detected; If the detection result represents that the ocular disc edema feature exists in the fundus image to be detected, determining that the risk level of the hypertensive retinopathy of the target object is level IV; If the detection result indicates that the characteristic of optic disc edema does not exist in the fundus image to be detected, extracting bleeding points and soft seepage characteristics in the fundus image to be detected; If the extraction result represents that the bleeding point and the soft seepage feature exist in the fundus image to be detected and the bleeding point is a strip-shaped bleeding point, determining that the risk level of the hypertensive retinopathy of the target object is level III.
  4. 4. The method according to claim 2, wherein the performing an arteriovenous segmentation process on the fundus image to be measured to generate an arteriovenous segmentation result comprises: Performing image preprocessing on the fundus image to be detected to generate a fundus blood vessel image; Performing example segmentation on the fundus blood vessel image to obtain a blood vessel example segmentation result, wherein the blood vessel example segmentation result is used for indicating a blood vessel segmentation result with blood vessel attributes output from a video disc boundary; Calibrating the blood vessel attribute of each blood vessel in the blood vessel example segmentation result, and outputting a blood vessel example segmentation result after calibration; numbering all blood vessels in the blood vessel example segmentation result based on a preset rule, and outputting a blood vessel marking result; And identifying an arteriovenous vessel based on the vessel marking result to obtain an arteriovenous segmentation result.
  5. 5. The method of claim 2, wherein the step of determining the position of the substrate comprises, Extracting the crossing positions of all the arteries and veins from the artery and vein segmentation result to obtain a plurality of crossing positions; if at least one of the plurality of cross positions has a cross compression point, determining that the risk level of the hypertensive retinopathy of the target object is level II based on the cross compression point; If all the crossing positions do not have crossing compression points, determining the blood vessel parameters of the arterial and venous main blood vessels based on the arterial and venous segmentation result, predicting by using a classification model based on the blood vessel parameters, and outputting a classification result of the hypertensive retinopathy; And determining that the risk level of the hypertensive retinopathy of the target object is level I based on the hypertensive retinopathy classification result.
  6. 6. The method of claim 5, wherein determining the vessel parameters of the arterial and venous main vessel based on the arterial and venous segmentation results comprises: Based on the arteriovenous segmentation result, the arteriovenous main blood vessels at the two ends of the video disc are identified to obtain two groups of arteriovenous main blood vessels, wherein the arteriovenous main blood vessels are used for indicating the arterial main blood vessels and the venous main blood vessels which are positioned at any end of the video disc and have adjacent relation; For any group of arterial and venous trunk blood vessels, respectively calculating a first arterial blood vessel parameter of an arterial trunk blood vessel and a first venous blood vessel parameter of a venous trunk blood vessel at a preset distance from a central point of a video disc, determining an arterial and venous parameter ratio based on the first arterial blood vessel parameter and the first venous blood vessel parameter, and obtaining a plurality of arterial and venous parameter ratios corresponding to different preset distances based on the arterial and venous parameter ratio corresponding to each preset distance, wherein the arterial and venous parameter ratio comprises an arterial and venous vessel diameter ratio and an arterial and venous average brightness ratio; for any group of arterial and venous trunk blood vessels, respectively calculating the average curvature corresponding to the arterial trunk blood vessel and the average curvature corresponding to the venous trunk blood vessel at the preset distance from the center point of the optic disc, determining the average curvature corresponding to the arterial trunk blood vessel and the average curvature corresponding to the venous trunk blood vessel as the arterial and venous average curvature corresponding to the preset distance; and taking the ratio of the arteriovenous parameters and the average tortuosity of the arteriovenous parameters as the blood vessel parameters of the arteriovenous main blood vessel in the fundus image to be measured.
  7. 7. The method according to claim 5, wherein the classification model is obtained by: Acquiring a plurality of target fundus images; Performing image preprocessing on the target fundus image to generate a target fundus blood vessel image aiming at any one of the target fundus images; an arteriovenous blood vessel segmentation process is carried out on the target fundus blood vessel image to generate an arteriovenous segmentation result, and an arteriovenous vessel diameter ratio, an arteriovenous average brightness ratio and an arteriovenous average curvature are determined based on the arteriovenous segmentation result; If one of the arteriovenous vessel diameter ratio, the arteriovenous average brightness ratio and the arteriovenous average curvature meets a preset condition, determining that the target fundus image has hypertensive retinopathy, and determining the arteriovenous vessel diameter ratio, the arteriovenous average brightness ratio and the arteriovenous average curvature corresponding to the target fundus image as a first training sample; if all three of the arteriovenous vessel diameter ratio, the arteriovenous average brightness ratio and the arteriovenous average curvature do not meet preset conditions, determining that the target fundus image does not have hypertensive retinopathy, and determining the arteriovenous vessel diameter ratio, the arteriovenous average brightness ratio and the arteriovenous average curvature corresponding to the target fundus image as a second training sample; And performing machine learning based on a first training sample or a second training sample corresponding to each target fundus image in the plurality of target fundus images, constructing a corresponding function model, and generating a classification model.
  8. 8. An evaluation device for the grade of hypertensive retinopathy is characterized in that, The detection module is used for detecting the characteristics of the hypertensive retinopathy of the fundus image to be detected of the target object; The determining module is used for determining the risk level of the hypertensive retinopathy of the target object based on the hypertensive retinopathy characteristics if the detection result represents that the hypertensive retinopathy characteristics exist in the fundus image to be detected.
  9. 9. An electronic device comprising a processor, a memory for storing instructions executable by the processor, the processor for reading the executable instructions from the memory and executing the instructions to implement the method of any of claims 1-7.
  10. 10. A computer readable medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-7.

Description

Evaluation method and device for hypertensive retinopathy grade Technical Field The invention belongs to the technical field of image processing, and particularly relates to a method and a device for evaluating a hypertensive retinopathy grade. Background The prior art is based on the fact that the diagnosis of the hypertensive retinopathy by fundus images is carried out by visual reading of doctors. However, the naked eye can only diagnose whether the patient has hypertension based on fundus images, and there is no way to effectively grade the hypertensive retinopathy. Different ocular fundus abnormalities reflect different courses of hypertension. Traditional Keith-Wagener-Barker hypertensive retinopathy grading criteria include grade I retinal arterioles light/medium constriction with arteriole ratio > =1:2, grade ii retinal arterioles medium, severe constriction (local or diffuse), with arteriole-venous ratio <1:2 or arteriole cross compression, grade III retinal soft exudation or flame-like hemorrhage, grade IV binocular optic disc oedema. Disclosure of Invention Aiming at the problems existing in the prior art, the embodiment of the invention provides a method and a device for evaluating the grade of hypertensive retinopathy, which are based on image processing technologies such as computer vision, deep learning and the like, and can automatically and quickly analyze the hypertensive retinopathy in fundus images, thereby greatly saving the time of a doctor for reading the film, enabling the evaluation to be more efficient, avoiding subjective differences among doctors, realizing consistency evaluation of fundus images and improving the accuracy of grade evaluation. According to a first aspect of the embodiment of the invention, a method for evaluating a hypertensive retinopathy level is provided, wherein a hypertensive retinopathy feature detection is performed on a fundus image to be tested of a target object, and if a detection result represents that the hypertensive retinopathy feature exists in the fundus image to be tested, the hypertensive retinopathy risk level of the target object is determined based on the hypertensive retinopathy feature. Optionally, performing first hypertension retinopathy feature detection on a fundus image to be detected of a target object, determining a hypertension retinopathy risk level of the target object based on a first hypertension retinopathy feature if the first hypertension retinopathy feature detection result represents that the first hypertension retinopathy feature exists in the fundus image to be detected, performing artery and vein segmentation processing on the fundus image to be detected if the first hypertension retinopathy feature detection result represents that the first hypertension retinopathy feature does not exist in the fundus image to be detected, generating an artery and vein segmentation result, performing second hypertension retinopathy feature detection on the artery and vein segmentation result, and determining the hypertension retinopathy risk level of the target object based on the second hypertension retinopathy feature if the second hypertension retinopathy feature detection result represents that the second hypertension retinopathy feature exists in the fundus image to be detected. Optionally, identifying characteristics of optic disc edema in the fundus image to be tested, determining that hypertensive retinopathy exists in the fundus image to be tested if the identification result indicates that the characteristics of optic disc edema exist in the fundus image to be tested, extracting bleeding points and soft seepage from the fundus image to be tested if the identification result indicates that the characteristics of optic disc edema do not exist in the fundus image to be tested, determining shapes of the bleeding points if the extraction result indicates that the bleeding points and the soft seepage exist in the fundus image to be tested at the same time, and determining that the hypertensive retinopathy exists in the fundus image to be tested if the shapes of the bleeding points are strip-shaped. Optionally, detecting whether the eyeground image to be detected has the characteristic of optic disc edema, if the detection result indicates that the eyeground image to be detected has the characteristic of optic disc edema, determining that the risk level of the target object for the hypertensive retinopathy is IV level, if the detection result indicates that the eyeground image to be detected does not have the characteristic of optic disc edema, extracting the bleeding point and the soft permeability characteristic in the eyeground image to be detected, and if the extraction result indicates that the eyeground image to be detected has the characteristic of bleeding point and soft permeability and the bleeding point is strip-shaped bleeding point, determining that the risk level of the target object for the hypertensive retinopathy is