WO2021120753A1 - Method and apparatus for recognition of luminal area in choroidal vessels, device, and medium - Google Patents

Abstract

The present application relates to the field of artificial intelligence, and provided in the application are a method and an apparatus for recognition of a luminal area in choroidal vessels, a device, and a medium, the method comprising: acquiring a fundus image to be recognized; inputting the image into a U-Net based fundus segmentation model, and by means of the fundus segmentation model, performing choroid feature extraction and edge segmentation on the fundus image to be recognized, to obtain a segmented fundus image; by means of a fundus fovea centralis recognition model, recognizing a fovea centralis area in the segmented fundus image, and on the basis of the fovea centralis area, cutting a first fundus choroid image from the segmented fundus image; by means of a Niblack local threshold algorithm, performing binarization processing on the first fundus choroid image, to obtain a first choroid binary image, and extracting a first luminal area from the first choroid binary image; and recognizing a first luminal area image. In the present application, automatic recognition of a luminal area in fundus choroidal vessels in a fundus image is made possible. The present application is suitable for such fields as smart medicine, and is able to further promote the construction of smart cities.

Description

Method, device, equipment and medium for identifying the lumen region of choroidal blood vessels

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on July 31, 2020, the application number is 202010761238.1, and the invention title is "Choroidal Vascular Lumen Region Recognition Method, Apparatus, Equipment, and Medium". The entire content of the Chinese patent application Incorporated in this application by reference.

Technical field

This application relates to the field of artificial intelligence image processing, and in particular to a method, device, equipment and medium for identifying the lumen region of choroidal blood vessels.

Background technique

The fundus choroid is located between the retina and the sclera. It is a soft, smooth, elastic and blood vessel-rich brown film. It starts from the serrated edge at the front and ends around the optic nerve at the back. The inner surface is connected by a layer of very smooth glass membrane and the retina. The pigment epithelial layer is connected, and the outside is connected with the sclera through a potential gap. The microfibrous platelets of the perichoroidal layer stretch and mix into the brown plate of the sclera, and blood vessels and nerves pass through it. The choroid is mainly composed of blood vessels, which provide oxygen and blood to the retina.

The inventor realizes that in the field of medicine, doctors often need to manually identify the lumen area of the fundus choroid blood vessels in the collected fundus photos based on experience, to determine the characteristics of the fundus choroid, and then perform other medical treatments based on the identified characteristics. Due to the fact that manual recognition has high requirements for doctors’ experience, low resolution of acquisition equipment, light ghosting, and other objective factors, the manual recognition of the characteristics of the fundus choroidal vessel lumen region is biased and the recognition accuracy is low.

Summary of the invention

This application provides a method, device, computer equipment, and storage medium for identifying the lumen region of choroidal blood vessels, which realizes automatic identification of the lumen region of the fundus choroidal blood vessels in fundus images. This application is suitable for fields such as smart transportation or smart medical care. It can further promote the construction of smart cities, reduce the cost of manual identification, and improve the accuracy and reliability of identification.

A method for identifying the lumen region of choroidal blood vessels, including:

Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.

A device for identifying the lumen region of choroidal blood vessels, comprising:

The receiving module is configured to receive the fundus lumen identification request, and obtain the fundus image to be identified in the fundus lumen identification request;

An input module for inputting the fundus image to be recognized into a U-Net-based fundus segmentation model, and performing choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmentation image;

The interception module is used to perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and cut out from the fundus segmented image according to the fovea area The first fundus choroid image;

The binary module is used to binarize the first fundus choroid image by the Niblack local threshold algorithm to obtain the first choroid binary image, and extract the first tube from the first choroid binary image Cavity area

The recognition module is used to recognize the first lumen area image containing the lumen area of the fundus choroidal blood vessels from the fundus image to be identified according to the first lumen area.

A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:

Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.

One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.

The method, device, computer equipment, and storage medium for identifying the lumen region of choroidal blood vessels provided in this application acquire the fundus image to be identified in the fundus lumen identification request by receiving the fundus lumen identification request; The fundus image input is based on the U-Net fundus segmentation model, and the fundus image to be identified is subjected to choroidal feature extraction and edge segmentation through the fundus segmentation model to obtain the fundus segmentation image; the fundus segmentation image is obtained through the fundus fovea recognition model Perform image recognition, identify the foveal area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area; use the Niblack local threshold algorithm to determine the first fundus choroidal image. The fundus choroid image is binarized to obtain a first choroid binary image, and a first lumen area is extracted from the first choroid binary image; according to the first lumen area, from the to-be-identified The first lumen region image containing the lumen region of the fundus choroidal blood vessels is recognized in the fundus image, so the automatic recognition of the lumen region of the fundus choroidal blood vessels in the fundus image is realized through the fundus segmentation model based on U-Net, the fundus The fovea recognition model and the Niblack local threshold algorithm can quickly and accurately identify the luminal area of the fundus choroidal blood vessels to determine the characteristics of the fundus choroid. This reduces the cost of manual identification and improves the accuracy and reliability of recognition.

The details of one or more embodiments of the present application are presented in the following drawings and description, and other features and advantages of the present application will become apparent from the description, drawings and claims.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor.

FIG. 1 is a schematic diagram of the application environment of the method for identifying the lumen region of choroidal blood vessels in an embodiment of the present application;

2 is a flowchart of a method for identifying the lumen region of choroidal blood vessels in an embodiment of the present application;

3 is a flowchart of a method for identifying the lumen region of choroidal blood vessels in another embodiment of the present application;

4 is a flowchart of step S20 of the method for identifying the lumen region of choroidal blood vessels in an embodiment of the present application;

5 is a flowchart of step S203 of the method for identifying the lumen region of choroidal blood vessels in an embodiment of the present application;

6 is a flowchart of step S30 of the method for identifying the lumen region of choroidal blood vessels in an embodiment of the present application;

FIG. 7 is a flowchart of step S70 of the method for identifying the lumen region of the choroidal blood vessel in an embodiment of the present application;

Fig. 8 is a schematic block diagram of a device for recognizing the lumen region of choroidal blood vessels in an embodiment of the present application;

Fig. 9 is a schematic diagram of a computer device in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

The method for identifying the lumen region of choroidal blood vessels provided in this application can be applied in the application environment as shown in FIG. 1, in which the client (computer equipment) communicates with the server through the network. Among them, the client (computer equipment) includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, cameras, and portable wearable devices. The server can be implemented as an independent server or a server cluster composed of multiple servers.

In one embodiment, as shown in FIG. 2, a method for identifying the lumen region of choroidal blood vessels is provided, and the technical solution mainly includes the following steps S10-S50:

S10. A fundus lumen identification request is received, and a fundus image to be identified in the fundus lumen identification request is acquired.

Understandably, the OCT scan image of the fundus collected by the OCT device, and the OCT scan image of the fundus collected by the enhanced mode of the OCT device can collect more morphological features of the fundus choroid. After the OCT scan image of the fundus is collected, it is necessary When the fundus choroidal vessel lumen region is recognized on the OCT scan image of the fundus, the fundus lumen recognition request is triggered, wherein the fundus lumen recognition request includes the fundus image to be recognized, and the fundus to be recognized The image is the captured OCT scan image of the fundus and the image of the luminal area of the fundus choroidal blood vessel needs to be recognized. The trigger mode can be set according to requirements, such as automatically triggering after the fundus image to be recognized is collected, or after all the fundus images are collected. After the fundus image is recognized, it is triggered by clicking the OK button.

Wherein, the fundus image to be recognized is a multi-channel color fundus photograph or a black-and-white fundus photograph. In one embodiment, acquiring the fundus image to be recognized in the fundus lumen recognition request includes an OCT scan image of the acquired fundus After preprocessing (filter denoising or/and image enhancement), such as Gaussian filter denoising, gamma transform algorithm correction, Laplace algorithm correction, etc., the preprocessed OCT scan image of the fundus is obtained as the waiting Recognizing the fundus image, in this way, can more reflect the blood vessel information of the fundus choroid.

S20: Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmentation image.

Understandably, the fundus segmentation model is a trained convolutional neural network model based on the U-Net model, that is, the network structure of the fundus segmentation model includes the network structure of the U-Net model, that is, the fundus The network structure of the segmentation model is the network structure of the model improved on the basis of the network structure of the U-Net model. The U-Net model is conducive to image segmentation and requires less training set to achieve end-to-end The network structure of the end training, the fundus segmentation model extracts the choroid feature extraction of the fundus image to be recognized, and the choroid feature is the feature of the choroid layer and surrounding texture and shape information in the fundus choroid, and the choroid is extracted The feature is the use of continuous convolution and pooling down-sampling layers to extract the feature information in the fundus image to be recognized, and gradually map the feature information to high dimensions to obtain the highest dimensional and richest fundus image corresponding to the fundus image to be recognized The feature vector array of the feature information is to obtain a high-dimensional feature map. The edge segmentation process is as follows: first, the high-dimensional feature map is continuously deconvolved and the up-sampling layer is up-sampled to the fundus to be identified. The fundus output image with the same dimension of the image; secondly, edge detection is added in the upsampling process and the edge feature information is enhanced; finally, the fundus output image with the enhanced edge feature information is image segmented (enhanced segmentation accuracy ) To obtain the segmented fundus image.

In an embodiment, as shown in FIG. 4, before the step S20, that is, before the input of the fundus image to be recognized into the U-Net-based fundus segmentation model, the method includes:

S201: Obtain a fundus image sample; the fundus image sample is associated with an edge line label and an area label.

Understandably, the fundus image sample is a collected historical OCT scan image containing the fundus choroid layer or an OCT scan image after preprocessing, and one fundus image sample is associated with one edge line label, and The edge line label is a set of manually labeled coordinate positions of points corresponding to the upper edge line and the lower edge line of the fundus choroid layer contained in the fundus image sample, and one fundus image sample is associated with one area label, so The area label is a set of manually labeled coordinate positions corresponding to the area range of the fundus choroid layer contained in the fundus image sample.

S202: Input the fundus image sample into a U-Net-based convolutional neural network model containing initial parameters.

Understandably, the fundus image sample is input to the convolutional neural network model, the convolutional neural network model is a model constructed based on the U-Net model, and the convolutional neural network model includes the initial parameters, The initial parameters include the network structure of the U-Net model. In one embodiment, through transfer learning, the transfer learning (Transfer Learning, TL) is applied in this field by using the parameters of existing training models in other fields. In the task of obtaining all parameters in a trained U-Net model, and using all the obtained parameters as the initial parameters, the number of iterations of the model is shortened, the training process is simplified, and the training efficiency is improved.

S203. Extract the choroidal feature in the fundus image sample through the convolutional neural network model, and obtain the region recognition result, fundus feature vector map, and fusion feature vector output by the convolutional neural network model according to the choroidal feature Figure.

Understandably, the choroidal feature in the fundus image sample is extracted through the convolutional neural network model, and the convolutional neural network model includes at least four of the down-sampling layers, and the down-sampling layers include Convolutional layer and pooling layer, the down-sampling layer is the level of multi-dimensional extraction of the choroidal features through different convolution kernels and pooling parameters, the convolution of the convolutional layer of each down-sampling layer The nuclei are different, and the pooling parameters of the pooling layer of each down-sampling layer are also different. Each down-sampling layer will output a choroidal feature map corresponding to the down-sampling layer. The choroid feature map output by the sampling layer is convolved to obtain the high-dimensional feature map, that is, the high-dimensional feature map is a feature vector array of the highest dimension and richest feature information corresponding to the fundus image to be recognized , Determining the choroid feature map and the high-dimensional feature map corresponding to each of the down-sampling layers as the fundus choroid feature map corresponding to the fundus image sample;

Wherein, the convolutional neural network model includes an upsampling layer corresponding to the downsampling layer, that is, the number of downsampling layers and the number of upsampling layers included in the convolutional neural network model Similarly, the fundus choroidal feature map is subjected to continuous deconvolution processing, that is, up-sampling processing, until the fundus output image is output, each of the up-sampling layers will output a fundus feature map, and each of the upsampling layers will output a fundus feature map. The fundus feature map output by the sampling layer is determined to be the fundus feature vector map, and the fundus feature maps output by the two consecutive long sampling layers are fused to obtain a fused feature vector map. By comparing the fundus feature vector map Perform choroidal region recognition to obtain a region recognition result. The region recognition result includes the recognition probability value corresponding to each pixel in the fundus output image, and all the recognition probability values greater than the preset probability threshold correspond to the Pixels are marked in the fundus output image to obtain the recognition area in the area recognition result, and the recognition area and all the recognition probability values are determined as the area recognition result.

In one embodiment, as shown in FIG. 5, in the step S203, the choroidal feature in the fundus image sample is extracted by the convolutional neural network model to obtain the convolutional neural network model The region recognition result, fundus feature vector map, and fusion feature vector map output according to the choroid features include:

S2031: Extract the choroidal feature from the fundus image sample by using the convolutional neural network model to obtain a fundus choroidal feature map.

Understandably, the convolutional neural network model includes a first down-sampling layer, a second down-sampling layer, a third down-sampling layer, and a fourth down-sampling layer. The first down-sampling layer and the second down-sampling layer The layer, the third down-sampling layer and the fourth down-sampling layer all include two convolutional layers of 3×3 convolution kernels, two activation layers, and a pooling layer with a maximum pooling parameter of 2×2, The fundus image sample is input to the first down-sampling layer for convolution to obtain a 64-dimensional first choroid feature map; the first choroidal feature map is input to the second down-sampling layer for convolution to obtain 128 dimensions The second choroid feature map; the second choroid feature map is input to the third down-sampling layer for convolution to obtain a 256-dimensional third choroid feature map; the third choroid feature map is input to the first Four down-sampling layers are convolved to obtain a 512-dimensional fourth choroidal feature map; a 3×3 convolution kernel convolution layer and an activation layer are performed on the fourth choroidal feature map to obtain the 1024-dimensional high Dimensional feature map, determining the first choroidal feature map, the second choroidal feature map, the third choroidal feature map, the fourth choroidal feature map, and the high-dimensional feature map as the fundus choroid Feature map.

S2032: Up-sampling and splicing the fundus choroid feature map through the convolutional neural network model to obtain the fundus feature vector map.

Understandably, the up-sampling and splicing is to deconvolve the fundus choroid feature map to generate an intermediate fundus feature map with the same dimension as the adjacent choroid feature map, and to splice it with the choroid feature map. During the splicing process, the dimension will become twice the original dimension. At this time, it needs to be convolved again to obtain the fundus feature map to ensure that the dimension of the processed fundus feature map is the same as the dimension before the splicing operation. The next up-sampling and splicing are performed until the final dimension of the fundus image sample is the same, the fundus choroid feature map is up-sampled and spliced, and the fundus feature vector map is finally output.

Wherein, the convolutional neural network model includes a first upsampling layer, a second upsampling layer, a third upsampling layer, and a fourth upsampling layer, and the fourth upsampling layer reverses the high-dimensional feature map. Convolution to obtain a 512-dimensional fourth intermediate fundus feature map, which is spliced with the 512-dimensional fourth choroidal feature map, and then passes through a 3×3 convolution kernel convolution layer And an activation layer to obtain a 512-dimensional fourth fundus feature map; the third up-sampling layer deconvolves the fourth fundus feature map to obtain a 256-dimensional third middle fundus feature map, and the second The three middle fundus feature maps are spliced with the 256-dimensional third choroidal feature map, and then through a 3×3 convolution kernel convolution layer and an activation layer, a 256-dimensional third fundus feature map is obtained; The second up-sampling layer deconvolves the third fundus feature map to obtain a 128-dimensional second intermediate fundus feature map, and stitches the second intermediate fundus feature map with the 128-dimensional second choroidal feature map , And then go through a convolutional layer of a 3×3 convolution kernel and an activation layer to obtain a second fundus feature map of 128 dimensions; the first up-sampling layer deconvolves the second fundus feature map to obtain A 64-dimensional first intermediate fundus feature map, which is spliced with the 64-dimensional first choroidal feature map, and then passes through a convolutional layer of a 3×3 convolution kernel and an activation layer , Obtain the first fundus feature map of 64 dimensions; convolve the fundus feature map with a 1×1 convolution kernel convolution layer to obtain the fundus output image, and combine the fourth fundus feature map, The third fundus feature map, the second fundus feature map, the first fundus feature map, and the fundus output image are determined to be the fundus feature vector map.

S2033: Perform choroidal region recognition on the fundus feature vector map through the convolutional neural network model to obtain the region recognition result, and at the same time perform fusion processing on the fundus feature vector map to obtain the fused feature vector map.

Understandably, according to the fundus feature vector map, the recognition probability value corresponding to each pixel in the fundus output image is calculated, and all the pixels corresponding to the recognition probability value greater than the preset probability threshold are calculated. Marking is performed on the fundus output image to obtain the recognition area in the area recognition result, and the recognition area and all the recognition probability values are determined as the area recognition result. Wherein, the first fundus feature map and the second fundus feature map are superimposed to obtain a first feature map to be fused, and the second fundus feature map is superimposed with the third fundus feature map to obtain a first fundus feature map. Two feature maps to be fused, the third fundus feature map and the fourth fundus feature map are superimposed to obtain a third feature map to be fused, and the first feature map to be fused and the second feature map to be fused Figure, the third to-be-fused feature map and the fourth fundus feature map are fused to obtain the fused feature vector map. The fusion is feature fusion, that is, the extracted feature information is analyzed, processed and integrated to obtain commonality A fusion feature vector map with very obvious features, and the size of the fusion feature vector map is consistent with the size of the fundus output image.

In this way, the present application realizes that the choroidal feature is extracted from the fundus image sample through the convolutional neural network model to obtain a fundus choroidal feature map; and the fundus choroidal feature map is upsampled through the convolutional neural network model And splicing to obtain the fundus feature vector map; perform choroidal region recognition on the fundus feature vector map through the convolutional neural network model to obtain the region recognition result, and at the same time perform fusion processing on the fundus feature vector map, The fusion feature vector graph is obtained, therefore, the accuracy of recognition can be improved, the number of training iterations can be reduced, and the training efficiency can be improved.

S204: Perform edge detection on the fundus feature vector map through the convolutional neural network model to obtain an edge result, and simultaneously perform region segmentation on the fused feature vector map to obtain a region segmentation result.

Understandably, the edge detection is to identify points with obvious changes in feature vectors in the fundus feature vector map, and by performing the edge detection processing on the fundus feature vector map, it is possible to identify points with the fundus feature vector map. The coordinate point of the upper edge line and the coordinate point of the lower edge line in the vector diagram, and the probability value of each coordinate point with the upper edge line in the fundus feature vector diagram (confirmed as the probability value of the upper edge line) is calculated, and The probability value of each coordinate point corresponding to the bottom edge line in the fundus feature vector diagram (confirmed as the probability value of the bottom edge line) will be compared with the coordinate point of the top edge line and the coordinate point of the bottom edge line in the fundus feature vector diagram. Points, and the probability values of the coordinate points of the upper edge line and the coordinate points of the lower edge line in the fundus feature vector map are determined together as the edge result, and at the same time, the fusion feature vector map is segmented, the The region segmentation is based on the feature vector corresponding to each pixel in the fusion feature vector map, and the probability value of whether the coordinate of each pixel point in the fusion feature vector map corresponds to the area of the fundus choroid layer is calculated, and it will be confirmed The coordinates of the region of the fundus choroid layer are segmented, and the region segmentation result is obtained.

S205. Determine a classification loss value according to the area recognition result and the area label; determine an edge loss value according to the edge result and the edge line label; according to the area segmentation result and the area label, Determine the segmentation loss value.

Understandably, the region recognition result and the region label are input into the classification loss function in the convolutional neural network model, and the classification loss value is calculated by the classification loss function, and the classification loss function can be based on Demand setting, such as cross entropy loss function, input the edge result and the edge line label into the edge loss function in the convolutional neural network model, calculate the edge loss value through the edge loss function, and set The region segmentation result and the region label are input to the segmentation loss function in the convolutional neural network model, and the segmentation loss value is calculated by the segmentation loss function.

S206: Determine a total loss value according to the classification loss value, the edge loss value, and the segmentation loss value.

Understandably, the classification loss value, the edge loss value, and the segmentation loss value are input into the total loss function in the convolutional neural network model, and the total loss value is calculated by the total loss function ; The medium loss value is:

L=λ ₁ L ₁ +λ ₂ L ₂ +λ ₃ L ₃

Where λ ₁ is the weight of the classification loss value; L ₁ is the classification loss value; λ ₂ is the weight _{of the edge loss value; L 2} is the edge loss value; λ ₃ is the segmentation loss value The weight of; L ₃ is the segmentation loss value.

S207: When the total loss value does not reach the preset convergence condition, iteratively update the initial parameters of the convolutional neural network model, until the total loss value reaches the preset convergence condition, the subsequent convergence The convolutional neural network model is recorded as a fundus segmentation model.

Understandably, the convergence condition may be a condition that the value of the total loss value is small and will not drop after 1000 calculations, that is, the value of the total loss value is small and will not drop after 1000 calculations. When it does not fall anymore, stop training, and record the convolutional neural network model after convergence as the fundus segmentation model after training; the convergence condition may also be a condition that the total loss value is less than a set threshold , That is, when the total loss value is less than the set threshold, the training is stopped, and the convolutional neural network model after convergence is recorded as the fundus segmentation model after training. In this way, when the total loss value does not reach When the preset convergence conditions are set, the initial parameters of the iterative convolutional neural network model are continuously updated, and the convolutional neural network model is triggered to extract the choroidal features in the fundus image sample to obtain the convolutional neural network. The network model can continuously move closer to the accurate result based on the regional recognition result, fundus feature vector map and the step of fusing the feature vector map outputted by the choroidal features, so that the accuracy of recognition becomes higher and higher.

S30: Perform image recognition on the fundus segmented image using the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and cut out the first fundus from the fundus segmented image according to the fovea area Choroidal image.

Understandably, image recognition is performed on the fundus segmented image through the fundus fovea recognition model. The fundus fovea recognition model is a trained neural network model. The network structure of the fundus fovea recognition model can be based on requirements. Setting, for example, the fundus fovea recognition model can be the network structure of the YOLO (You Only Look Once) model, the network structure of the SSD (Single Shot MultiBox Detector) model, etc., because the fovea area is in the fundus segmented image It is preferred that the area of the fovea recognition model is smaller, so the network structure of the fundus fovea recognition model is preferably the network structure of the SSD (Single Shot MultiBox Detector) model, because the network structure of the SSD model is conducive to the identification of small objects, and according to the The obtained foveal area is intercepted from the fundus segmented image according to preset size parameters to obtain the first fundus choroid image. The size parameters can be set according to requirements, for example, the size of the foveal area The length of the center is 6000μm and the width is 1500μm.

In one embodiment, as shown in FIG. 6, in the step S30, the image recognition is performed on the fundus segmented image through the fundus fovea recognition model to identify the fovea area in the fundus segmented image, And extracting a first fundus choroid image from the fundus segmented image according to the fovea area, including:

S301: Input the segmented image of the fundus into an SSD-based fundus fovea recognition model.

Understandably, the fundus fovea recognition model is a trained neural network model based on an SSD model, and the fundus segmented image is input into the fundus fovea recognition model.

S302: Using the SSD algorithm, extract the fovea feature through the fundus fovea recognition model, and perform target detection according to the fovea feature to obtain the fovea area.

Understandably, through the SSD (Single Shot MultiBox Detector) algorithm, the SSD algorithm extracts the fovea feature of the fundus segmented image through feature maps of different scales, and the fovea feature is the same as that of the fundus. The characteristics of the foveal area of the choroid layer, and target detection is performed based on the fovea features, that is, the area is predicted by the method of clearly distinguishing the aspect ratio of the extracted fovea features, and finally the choroidal fovea containing the fundus is identified The foveal area.

S303, taking the fovea area as a center, and intercepting the first fundus choroid image from the fundus segmented image according to a preset size parameter.

Understandably, the size parameter can be set according to requirements. For example, the size parameter is 6000×1500, and the second is cut from the fundus segmented image according to a preset size parameter with the fovea region as the center. A fundus choroid image, for example, taking the size parameters of the center of the fovea region of 6000 μm in length and 1500 μm in width to intercept the first fundus choroid image from the fundus segmented image.

In this way, this application realizes that by inputting the fundus segmented image into an SSD-based fundus fovea recognition model; using the SSD algorithm, extracting the fovea features from the fundus fovea recognition model, and performing target detection based on the fovea features , Obtain the foveal area; take the foveal area as the center, and intercept the first fundus choroid image from the fundus segmented image according to preset size parameters, so that the foveal area can be automatically identified, and The fundus choroid image of the same size is cut out to facilitate subsequent identification and improve the accuracy of identification.

S40: Binarize the first fundus choroid image by using the Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image.

Understandably, binarization is performed on the first fundus choroid image to obtain a value corresponding to the first choroidal binary image, and the value of each pixel in the first choroidal binary image is 0 or 1, or It is displayed in two colors of white (corresponding to 0) or black (corresponding to 1), and extracting the black area in the first choroidal binary image to obtain the first lumen area.

Wherein, the binarization processing includes a processing operation of performing binarization calculation on each pixel in the first fundus choroid image by the Niblack local threshold algorithm, and the Niblack local threshold algorithm is for each pixel in the image. Each pixel is compared with the threshold calculated by the local area for binarization.

S50, according to the first lumen area, identify a first lumen area image containing a lumen area of the fundus choroidal blood vessel from the fundus image to be identified.

Understandably, marking is performed from the fundus image to be identified according to the coordinate position of the first lumen area, so that the first lumen area image can be obtained, and the first lumen area image is the mark fundus choroid Image of the lumen area of the blood vessel.

In this way, this application realizes that by receiving the fundus lumen recognition request, the fundus image to be recognized in the fundus lumen recognition request is obtained; the fundus image to be recognized is input into the U-Net-based fundus segmentation model, and the fundus segmentation model is based on U-Net. The fundus segmentation model performs choroid feature extraction and edge segmentation on the fundus image to be recognized to obtain a fundus segmented image; image recognition is performed on the fundus segmented image through the fundus fovea recognition model, and the fovea in the fundus segmentation image is identified Region, and extract the first fundus choroid image from the fundus segmentation image according to the fovea region; use the Niblack local threshold algorithm to binarize the first fundus choroid image to obtain the first choroid binary value Image, and extract the first lumen area from the first choroidal binary image; according to the first lumen area, identify the first lumen area containing the fundus choroidal blood vessels from the fundus image to be identified A lumen region image, therefore, realizes the automatic recognition of the lumen region of the fundus choroidal blood vessels in the fundus image. Through the U-Net-based fundus segmentation model, the fundus fovea recognition model and the Niblack local threshold algorithm, it can be quickly and accurately The luminal area of the fundus choroidal blood vessels is accurately recognized to determine the characteristics of the fundus choroid. In this way, the cost of manual identification is reduced, and the accuracy and reliability of identification are improved.

In an embodiment, as shown in FIG. 3, after the step S50, that is, after the first lumen region is extracted from the first choroidal binary image, the method further includes:

S60: Perform grayscale processing on the fundus image to be identified to obtain a grayscale image of the fundus.

Understandably, the multi-channel fundus image to be identified is subjected to grayscale processing to obtain the single-channel fundus grayscale image. For example, the fundus image to be identified includes RGB (Red Green Blue, red, green, and blue). (Color) three-channel image, the gray-scale binarization of the value corresponding to the same pixel in each channel image is performed to obtain the gray-scale value of the pixel, and a channel image is obtained, which is the fundus gray-scale image.

S70. Obtain a lumen adaptive threshold value in the first lumen region by an adaptive threshold method, and perform normalization processing on the fundus gray-scale image according to the lumen adaptive threshold value to obtain a first fundus image.

Understandably, the adaptive threshold method is a method that uses a local threshold value in an image to replace a global threshold value for image calculation. It is specifically aimed at images with excessively large changes in light and shadow, or images with less obvious color differences within a range. The normalization process is to calculate the average value of all gray values corresponding to pixels in the first lumen area by the adaptive threshold method, record it as the lumen adaptive threshold, and obtain The preset maximum grayscale value, the maximum grayscale value can be set according to requirements, preferably 255, and then calculated according to the grayscale normalization function in the adaptive threshold method in the fundus grayscale image and The normalized value of the gray level corresponding to each pixel, and finally output the first fundus image, and the first fundus image has the same size as the fundus image to be identified.

In one embodiment, as shown in FIG. 7, in the step S70, that is, the adaptive threshold method is used to obtain the adaptive threshold value of the lumen in the first lumen region, and automatically The adaptive threshold normalizes the gray-scale image of the fundus to obtain the first fundus image, including:

S701: Acquire the adaptive threshold of the lumen through an adaptive threshold method.

Understandably, the adaptive threshold method is a method that uses local thresholds in the image to replace global thresholds for image calculation, and calculates the average value of all gray values corresponding to pixels in the first lumen region, Record this as the adaptive threshold of the lumen.

S702: Acquire a gray value corresponding to each pixel in the fundus gray image and a preset maximum gray value.

Understandably, the fundus gray-scale image includes a gray-scale value corresponding to each pixel, and a preset maximum gray-scale value is obtained. The maximum gray-scale value can be set according to requirements, and is preferably 255.

S703. Input the lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value into a gray normalized model, and obtain all the corresponding pixels corresponding to each pixel. Said gray normalization value.

Understandably, the gray-level normalization model includes a gray-level normalization function, and the gray-level normalization value corresponding to each pixel point can be calculated by the gray-level normalization function.

In one embodiment, in the step S703, that is, the lumen adaptive threshold, the gray value corresponding to each pixel point, and the maximum gray value input gray level are normalized In the model, obtaining the normalized gray value corresponding to each pixel includes:

S7031. Input the lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value into a gray normalization function to obtain all the corresponding pixel points. The gray scale normalization value; the gray scale normalization function is:

among them,

f(x, y) is the gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

F(x, y) is the normalized gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

A is the adaptive threshold of the lumen;

B is the maximum gray value.

S704: Join all the normalized gray values according to the positions of the pixels to obtain the first fundus image.

Understandably, each of the gray-scale normalized values is spliced according to the positions of the corresponding pixel points to form a new image, and the image is determined as the first fundus image, so as to correct the gray fundus The degree image is corrected.

In this way, the present application realizes that through the adaptive threshold method, the fundus gray-scale image can be corrected, and the lumen area of the fundus choroidal blood vessel can be corrected, so that the lumen area of the fundus choroidal blood vessel can be more prominent, which is easy to identify and improve Improved recognition accuracy and reliability.

S80, extracting a second fundus choroid image from the first fundus image according to the fovea area.

Understandably, extracting from the first fundus image according to the coordinate area of the fovea region can obtain the second fundus choroid image.

S90: Perform binarization processing on the second fundus choroid image according to the Niblack local threshold method to obtain a second choroid binary image, and extract a second lumen region from the second choroid binary image.

Understandably, through the Niblack local threshold method, the second fundus choroid image is binarized to obtain the value corresponding to the second choroid binary image. The value of each pixel in the second choroid binary image is The value is 0 or 1, and can also be displayed in two colors of white (corresponding to 0) or black (corresponding to 1). The black area in the second choroidal binary image is extracted to obtain the second lumen area.

Wherein, the binarization processing further includes a processing operation of performing binarization calculation on each pixel in the second fundus choroid image by the Niblack local threshold algorithm.

S100, according to the second lumen area, identify a second lumen area image containing the lumen area of the fundus choroidal blood vessel from the fundus image to be identified.

Understandably, marking is performed from the fundus image to be identified according to the coordinate position of the second lumen area, so that the second lumen area image can be obtained, and the second lumen area image is the mark fundus choroid The image of the lumen area of the blood vessel, in this way, can more accurately determine the lumen area of the fundus choroidal blood vessel.

This application realizes that by receiving the fundus lumen recognition request, the fundus image to be recognized in the fundus lumen recognition request is obtained; the fundus image to be recognized is input into the U-Net-based fundus segmentation model, and the fundus segmentation is performed The model performs choroid feature extraction and edge segmentation on the fundus image to be recognized to obtain a fundus segmented image; image recognition is performed on the fundus segmented image through the fundus fovea recognition model to identify the fovea area in the fundus segmented image, And cut out the first fundus choroid image from the fundus segmented image according to the fovea region; use the Niblack local threshold algorithm to binarize the first fundus choroid image to obtain the first choroidal binary image, And extract the first lumen area from the first choroidal binary image; according to the first lumen area, identify the first tube containing the lumen area of the fundus choroidal blood vessel from the fundus image to be identified Cavity region image, performing grayscale processing on the fundus image to be identified to obtain a fundus grayscale image; using an adaptive threshold method to obtain the lumen adaptive threshold in the first lumen region, and according to the lumen The adaptive threshold normalizes the fundus grayscale image to obtain the first fundus image; extracts the second fundus choroid image from the first fundus image according to the foveal area; according to the Niblack local threshold method, Binarize the second fundus choroid image to obtain a second choroid binary image, and extract a second lumen area from the second choroid binary image; according to the second lumen area, The second lumen region image containing the lumen region of the fundus choroidal blood vessels is identified from the fundus image to be identified. Therefore, the U-Net-based fundus segmentation model, the fundus fovea recognition model, and two Niblack local Threshold algorithm processing and adaptive threshold method can correct the fundus image to be identified, so as to more accurately identify the lumen region of the fundus choroidal blood vessel, thus further improving the recognition accuracy and reliability.

In one embodiment, after the step S100, that is, after the second lumen area image containing the lumen area of the fundus choroidal blood vessels is identified from the fundus image to be identified according to the second lumen area ,include:

S110: Calculate the area of the luminal area of the fundus choroidal blood vessel in the second luminal area image to obtain the area of the luminal area, and calculate the area of the first fundus choroidal image to obtain the area of the choroidal area;

S120: Calculate the ratio of the area of the lumen region to the area of the choroid region to obtain a choroidal blood vessel index.

In this way, by using the identified second lumen region image, the lumen region area of the lumen region containing the fundus choroidal blood vessels is calculated, and the choroidal region area is calculated at the same time, and the ratio of the lumen region area and the choroidal region area is calculated to obtain the choroidal blood vessel Index, so that the doctor can carry out the next medical action, provides data indicators related to the fundus choroid.

In one embodiment, a device for identifying the lumen region of choroidal blood vessels is provided. The device for identifying the lumen region of choroidal blood vessels corresponds to the method for identifying the lumen region of choroidal blood vessels in the above-mentioned embodiment. As shown in FIG. 8, the device for identifying the lumen region of choroidal blood vessels includes a receiving module 11, an input module 12, an intercepting module 13, a binary module 14 and an identifying module 15. The detailed description of each functional module is as follows:

The receiving module 11 is configured to receive the fundus lumen identification request, and obtain the fundus image to be identified in the fundus lumen identification request;

The input module 12 is configured to input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

The intercepting module 13 is configured to perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and intercept the fundus segmented image according to the fovea area Get the first fundus choroid image;

The binary module 14 is used to binarize the first fundus choroid image by using the Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first choroid image from the first choroid image. Luminal area

The recognition module 15 is configured to recognize, from the fundus image to be recognized, a first lumen area image containing a lumen area of the fundus choroidal blood vessel according to the first lumen area.

For the specific definition of the device for identifying the lumen region of the choroidal blood vessel, please refer to the above definition of the method for identifying the lumen region of the choroidal blood vessel, which will not be repeated here. The various modules in the above-mentioned choroidal vessel lumen region recognition device can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. 9. The computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer readable instructions in the readable storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for identifying the lumen region of choroidal blood vessels is realized. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and running on the processor. When the processor executes the computer-readable instructions, the choroid in the above-mentioned embodiment is implemented. Method for identifying the lumen area of blood vessels.

In one embodiment, one or more readable storage media storing computer readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the method for identifying the lumen region of the choroidal blood vessel in the above-mentioned embodiment .

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile computer. In a readable storage medium or a volatile readable storage medium, when the computer readable instruction is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not a limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional units and modules is used as an example. In practical applications, the above functions can be allocated to different functional units and modules as needed. Module completion, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application, and should be included in Within the scope of protection of this application.

Claims (21)

1. A method for identifying the lumen region of choroidal blood vessels, which includes:

   Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

   Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

   Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

   Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

   According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
2. 3. The method for identifying the lumen region of choroidal blood vessels according to claim 1, wherein after extracting the first lumen region from the first choroidal binary image, the method further comprises:

   Performing grayscale processing on the fundus image to be identified to obtain a grayscale image of the fundus;

   Obtain the lumen adaptive threshold value in the first lumen region by the adaptive threshold method, and perform normalization processing on the fundus grayscale image according to the lumen adaptive threshold value to obtain the first fundus image;

   Extracting a second fundus choroid image from the first fundus image according to the foveal area;

   According to the Niblack local threshold method, binarize the second fundus choroid image to obtain a second choroid binary image, and extract the second lumen region from the second choroid binary image;

   According to the second lumen area, a second lumen area image containing the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
3. The method for recognizing the lumen region of choroidal blood vessels according to claim 1, wherein, before inputting the fundus image to be recognized into a U-Net-based fundus segmentation model, the method comprises:

   Obtaining a fundus image sample; the fundus image sample is associated with an edge line label and an area label;

   Input the fundus image sample into a U-Net-based convolutional neural network model containing initial parameters;

   Extracting the choroidal feature in the fundus image sample by using the convolutional neural network model, and obtaining the region recognition result, fundus feature vector map, and fusion feature vector map output by the convolutional neural network model according to the choroidal feature;

   Performing edge detection on the fundus feature vector map by using the convolutional neural network model to obtain an edge result, and at the same time performing region segmentation on the fused feature vector map to obtain a region segmentation result;

   Determine the classification loss value based on the region recognition result and the region label; determine the edge loss value based on the edge result and the edge line label; determine the edge loss value based on the region segmentation result and the region label Split loss value;

   Determine a total loss value according to the classification loss value, the edge loss value, and the segmentation loss value;

   When the total loss value does not reach the preset convergence condition, iteratively update the initial parameters of the convolutional neural network model, until the total loss value reaches the preset convergence condition, the subsequent convergence The convolutional neural network model is recorded as a fundus segmentation model.
4. The method for identifying the lumen region of choroidal blood vessels according to claim 3, wherein said extracting said choroidal features in said fundus image sample through said convolutional neural network model, and obtaining said convolutional neural network model according to The region recognition result, fundus feature vector map, and fusion feature vector map output by the choroid feature include:

   Extracting the choroidal feature from the fundus image sample by using the convolutional neural network model to obtain a fundus choroidal feature map;

   Up-sampling and splicing the fundus choroid feature map through the convolutional neural network model to obtain the fundus feature vector map;

   Perform choroidal region recognition on the fundus feature vector map through the convolutional neural network model to obtain the region recognition result, and at the same time perform fusion processing on the fundus feature vector map to obtain the fused feature vector map.
5. The method for recognizing the lumen region of choroidal blood vessels according to claim 1, wherein the image recognition is performed on the fundus segmented image through the fundus fovea recognition model to identify the fovea region in the fundus segmented image, and Extracting a first fundus choroid image from the fundus segmented image according to the fovea region includes:

   Inputting the segmented image of the fundus into an SSD-based fundus fovea recognition model;

   Using an SSD algorithm, extracting a fovea feature from the fundus fovea recognition model, and performing target detection based on the fovea feature to obtain the fovea area;

   Taking the foveal area as the center, and intercepting the first fundus choroid image from the fundus segmented image according to a preset size parameter.
6. The method for identifying the lumen region of choroidal blood vessels according to claim 2, wherein the adaptive threshold method is adopted to obtain the adaptive threshold value of the lumen in the first lumen region, and the adaptive threshold value of the lumen in the first lumen region is acquired according to the adaptive threshold The threshold value normalizes the gray-scale image of the fundus to obtain the first fundus image, including:

   Obtain the adaptive threshold of the lumen by an adaptive threshold method;

   Acquiring a gray value corresponding to each pixel in the fundus gray image and a preset maximum gray value;

   The lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value are input into the gray normalization model to obtain the gray corresponding to each pixel Degree normalization value;

   All the normalized gray values are spliced according to the positions of the pixels to obtain the first fundus image.
7. The method for identifying the lumen region of choroidal blood vessels according to claim 6, wherein said inputting said lumen adaptive threshold value, said gray value corresponding to each said pixel point and said maximum gray value In the gray-scale normalization model, obtaining the gray-scale normalized value corresponding to each pixel point includes:

   The lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value are input into the gray normalization function to obtain the gray corresponding to each pixel Degree normalization value; the gray normalization function is:

   

   among them,

   f(x, y) is the gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

   F(x, y) is the normalized gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

   A is the adaptive threshold of the lumen;

   B is the maximum gray value.
8. A device for identifying the lumen region of choroidal blood vessels, which comprises:

   The receiving module is configured to receive the fundus lumen identification request, and obtain the fundus image to be identified in the fundus lumen identification request;

   An input module for inputting the fundus image to be recognized into a U-Net-based fundus segmentation model, and performing choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmentation image;

   The interception module is used to perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and cut out from the fundus segmented image according to the fovea area The first fundus choroid image;

   The binary module is used to binarize the first fundus choroid image by the Niblack local threshold algorithm to obtain the first choroid binary image, and extract the first tube from the first choroid binary image Cavity area

   The recognition module is used to recognize the first lumen area image containing the lumen area of the fundus choroidal blood vessels from the fundus image to be identified according to the first lumen area.
9. A computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, wherein the processor implements the following steps when the processor executes the computer-readable instructions:

   Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

   Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

   Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

   Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

   According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
10. 9. The computer device of claim 9, wherein after the first lumen region is extracted from the first choroidal binary image, the processor further implements the following steps when executing the computer readable instruction:

    Performing grayscale processing on the fundus image to be identified to obtain a grayscale image of the fundus;

    Obtain a lumen adaptive threshold value in the first lumen region by using an adaptive threshold method, and perform normalization processing on the fundus grayscale image according to the lumen adaptive threshold value to obtain a first fundus image;

    Extracting a second fundus choroid image from the first fundus image according to the foveal area;

    According to the Niblack local threshold method, binarize the second fundus choroid image to obtain a second choroid binary image, and extract the second lumen region from the second choroid binary image;

    According to the second lumen area, a second lumen area image containing the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
11. 9. The computer device according to claim 9, wherein before the input of the fundus image to be recognized into the U-Net-based fundus segmentation model, the processor further implements the following steps when executing the computer-readable instructions:

    Obtaining a fundus image sample; the fundus image sample is associated with an edge line label and an area label;

    Input the fundus image sample into a U-Net-based convolutional neural network model containing initial parameters;

    Extracting the choroidal feature in the fundus image sample by using the convolutional neural network model, and obtaining the region recognition result, fundus feature vector map, and fusion feature vector map output by the convolutional neural network model according to the choroidal feature;

    Performing edge detection on the fundus feature vector map by using the convolutional neural network model to obtain an edge result, and at the same time performing region segmentation on the fused feature vector map to obtain a region segmentation result;

    Determine the classification loss value based on the region recognition result and the region label; determine the edge loss value based on the edge result and the edge line label; determine the edge loss value based on the region segmentation result and the region label Split loss value;

    Determine a total loss value according to the classification loss value, the edge loss value, and the segmentation loss value;

    When the total loss value does not reach the preset convergence condition, iteratively update the initial parameters of the convolutional neural network model, until the total loss value reaches the preset convergence condition, the subsequent convergence The convolutional neural network model is recorded as a fundus segmentation model.
12. The computer device according to claim 11, wherein said extracting said choroidal feature in said fundus image sample by said convolutional neural network model, and obtaining the output of said convolutional neural network model according to said choroidal feature Region recognition results, fundus feature vector maps and fusion feature vector maps, including:

    Extracting the choroidal feature from the fundus image sample by using the convolutional neural network model to obtain a fundus choroidal feature map;

    Up-sampling and splicing the fundus choroid feature map through the convolutional neural network model to obtain the fundus feature vector map;

    Perform choroidal region recognition on the fundus feature vector map through the convolutional neural network model to obtain the region recognition result, and at the same time perform fusion processing on the fundus feature vector map to obtain the fused feature vector map.
13. The computer device according to claim 11, wherein the image recognition is performed on the fundus segmented image through the fundus fovea recognition model, and the fovea area in the fundus segmented image is recognized, and the fovea area is determined based on the fovea area. The interception of the first fundus choroid image from the fundus segmented image includes:

    Inputting the segmented image of the fundus into an SSD-based fundus fovea recognition model;

    Using an SSD algorithm, extracting a fovea feature from the fundus fovea recognition model, and performing target detection based on the fovea feature to obtain the fovea area;

    Taking the foveal area as the center, and intercepting the first fundus choroid image from the fundus segmented image according to a preset size parameter.
14. The computer device according to claim 10, wherein the adaptive threshold value of the lumen in the first lumen region is obtained by the adaptive threshold method, and the retinal gray is determined according to the adaptive threshold value of the lumen. The degree image is normalized to obtain the first fundus image, including:

    Obtain the adaptive threshold of the lumen by an adaptive threshold method;

    Acquiring a gray value corresponding to each pixel in the fundus gray image and a preset maximum gray value;

    The lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value are input into the gray normalization model to obtain the gray corresponding to each pixel Degree normalization value;

    All the normalized gray values are spliced according to the positions of the pixels to obtain the first fundus image.
15. The computer device according to claim 14, wherein said inputting said lumen adaptive threshold value, said gray value corresponding to each said pixel and said maximum gray value into a gray normalization model In the step, obtaining the normalized gray value corresponding to each pixel includes:

    The lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value are input into the gray normalization function to obtain the gray corresponding to each pixel Degree normalization value; the gray normalization function is:

    

    among them,

    f(x, y) is the gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

    F(x, y) is the normalized gray value corresponding to the pixel with the coordinate (x, y) in the fundus gray image;

    A is the adaptive threshold of the lumen;

    B is the maximum gray value.
16. One or more readable storage media storing computer readable instructions, where when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:

    Receiving the fundus lumen identification request, and acquiring the fundus image to be identified in the fundus lumen identification request;

    Input the fundus image to be recognized into a U-Net-based fundus segmentation model, and perform choroid feature extraction and edge segmentation on the fundus image to be recognized through the fundus segmentation model to obtain a fundus segmented image;

    Perform image recognition on the fundus segmented image through the fundus fovea recognition model, identify the fovea area in the fundus segmented image, and extract the first fundus choroid image from the fundus segmented image according to the fovea area ；

    Binarize the first fundus choroid image by Niblack local threshold algorithm to obtain a first choroid binary image, and extract the first lumen region from the first choroid binary image;

    According to the first lumen area, the first lumen area image including the lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
17. The readable storage medium according to claim 16, wherein after the first lumen region is extracted from the first choroidal binary image, when the computer-readable instructions are executed by one or more processors , So that the one or more processors further execute the following steps:

    Performing grayscale processing on the fundus image to be identified to obtain a grayscale image of the fundus;

    Obtain the lumen adaptive threshold value in the first lumen region by the adaptive threshold method, and perform normalization processing on the fundus grayscale image according to the lumen adaptive threshold value to obtain the first fundus image;

    Extracting a second fundus choroid image from the first fundus image according to the foveal area;

    According to the Niblack local threshold method, binarize the second fundus choroid image to obtain a second choroid binary image, and extract the second lumen region from the second choroid binary image;

    According to the second lumen area, a second lumen area image containing a lumen area of the fundus choroidal blood vessel is identified from the fundus image to be identified.
18. The readable storage medium according to claim 16, wherein, before the input of the fundus image to be recognized into the U-Net-based fundus segmentation model, when the computer-readable instructions are executed by one or more processors, So that the one or more processors further execute the following steps:

    Obtaining a fundus image sample; the fundus image sample is associated with an edge line label and an area label;

    Input the fundus image sample into a U-Net-based convolutional neural network model containing initial parameters;

    Extracting the choroidal feature in the fundus image sample by using the convolutional neural network model, and obtaining the region recognition result, fundus feature vector map, and fusion feature vector map output by the convolutional neural network model according to the choroidal feature;

    Performing edge detection on the fundus feature vector map through the convolutional neural network model to obtain an edge result, and at the same time performing region segmentation on the fused feature vector map to obtain a region segmentation result;

    Determine the classification loss value based on the region recognition result and the region label; determine the edge loss value based on the edge result and the edge line label; determine the edge loss value based on the region segmentation result and the region label Split loss value;

    Determine a total loss value according to the classification loss value, the edge loss value, and the segmentation loss value;

    When the total loss value does not reach the preset convergence condition, iteratively update the initial parameters of the convolutional neural network model, until the total loss value reaches the preset convergence condition, the subsequent convergence The convolutional neural network model is recorded as a fundus segmentation model.
19. The readable storage medium according to claim 18, wherein said extracting said choroidal feature in said fundus image sample through said convolutional neural network model, and acquiring said convolutional neural network model according to said choroidal feature The output area recognition result, fundus feature vector map and fusion feature vector map include:

    Extracting the choroidal feature from the fundus image sample by using the convolutional neural network model to obtain a fundus choroidal feature map;

    Up-sampling and splicing the fundus choroid feature map through the convolutional neural network model to obtain the fundus feature vector map;

    Perform choroidal region recognition on the fundus feature vector map through the convolutional neural network model to obtain the region recognition result, and at the same time perform fusion processing on the fundus feature vector map to obtain the fused feature vector map.
20. The readable storage medium according to claim 18, wherein the image recognition is performed on the fundus segmented image by the fundus fovea recognition model, the fovea area in the fundus segmented image is recognized, and the center The concave region intercepts the first fundus choroid image from the fundus segmented image, including:

    Inputting the segmented image of the fundus into an SSD-based fundus fovea recognition model;

    Using an SSD algorithm, extracting a fovea feature from the fundus fovea recognition model, and performing target detection based on the fovea feature to obtain the fovea area;

    Taking the foveal area as the center, and intercepting the first fundus choroid image from the fundus segmented image according to a preset size parameter.
21. The readable storage medium according to claim 18, wherein the adaptive threshold method is used to obtain a lumen adaptive threshold in the first lumen region, and the lumen adaptive threshold is adjusted according to the lumen adaptive threshold. The gray-scale image of the fundus is normalized to obtain the first fundus image, including:

    Obtain the adaptive threshold of the lumen by an adaptive threshold method;

    Acquiring a gray value corresponding to each pixel in the fundus gray image and a preset maximum gray value;

    The lumen adaptive threshold, the gray value corresponding to each pixel and the maximum gray value are input into the gray normalization model to obtain the gray corresponding to each pixel Degree normalization value;

    All the normalized gray values are spliced according to the positions of the pixels to obtain the first fundus image.

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