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36 results about "Optic disc segmentation" patented technology
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Fundus image optic cup and optic disk segmentation method and system for assisting glaucoma screening
ActiveCN110992382AEfficient Multi-Size ExtractionBoost backpropagationImage enhancementImage analysisInformation processingGlaucoma screening
The invention discloses a fundus image optic disc segmentation method and a system for assisting glaucoma screening, and relates to the technical field of image information processing. The fundus image optic disc segmentation method comprises the steps that a plurality of fundus images are collected and preprocessed, and a training image sample set and a verification image sample set are obtained;training of a constructed W-Net-Mcon full convolutional neural network by using the training image sample set to obtain an optimal W-Net-Mcon full convolutional neural networkis carried out; preprocessing the fundus image to be segmented, and inputting the preprocessed fundus image to be segmented into the optimal W-Net-Mcon full convolutional neural network to obtain a prediction target result image; Processing prediction target result graph by utilizing polar coordinate inverse transformation and ellipse fitting to obtain final segmentation result so as to obtain cup-to-disk ratio and finally obtain glaucoma preliminary screening result. According to the method, image semantic information can be effectively extracted in a multi-size mode, fusion of features of different levels, fusion of global features and detail features and encouragement of feature multiplexing are carried out, gradient back propagation is improved, and the image segmentation precision is improved.
Owner:SICHUAN UNIV
A retinal fundus image cup/disc ratio automatic evaluation method
InactiveCN109829877AImprove accuracyImprove robustnessImage enhancementImage analysisNerve networkOptic disc segmentation
The invention discloses a retinal fundus image cup / disc ratio automatic evaluation method. The method comprises the following steps: A, extracting an optic disk area image from a retinal fundus image;B, establishing and training a optic disc optic cup segmentation network based on the deep convolutional neural network; C, acquiring a to-be-detected optic disc area image from the to-be-detected retina fundus image according to the step A, and inputting the to-be-detected optic disc area image into an optic disc optic cup segmentation network to output a to-be-detected optic disc segmentation mask image and a to-be-detected optic cup segmentation mask image; And step D, calculating the cup-disc ratio of the retinal fundus image according to the to-be-detected optic disc segmentation mask image and the to-be-detected optic cup segmentation mask image. The method is high in operation speed, good in effect, free of manual participation, low in cost and high in universality, and can be widely applied to auxiliary screening of glaucoma.
Owner:CENT SOUTH UNIV
Automatic cup-to-disc ratio measurement system
InactiveCN102112044AFast and precise CDR valuesLow costImage enhancementImage analysisOptic disc segmentationRetina
A two-dimensional retinal fundus image of the retinal fundus of an eye is processed by optic disc segmentation (2) followed by optic cup segmentation 4. Data derived from the optic disc segmentation (i.e. the output of the disc segmentation (2) and / or data derived from the output of the optic disc segmentation step, e.g. by a smoothing operation (3)) and data derived from the output of the optic cup segmentation (i.e. the output of the cup segmentation (4) and / or data derived from the output of the optic disc segmentation, e.g. by a smoothing operation (5)) are fed to an adaptive model which has been trained to generate from such inputs a value indicative of cup-to-disc ratio (CDR) of the eye. The CDR is indicative of glaucoma. Thus, the method can be used to screen patients for glaucoma.
Owner:科学、技术与研究机构 +2
Fundus retina blood vessel recognition and quantification method, device and equipment and storage medium
PendingCN111340789AImprove recognition accuracyHigh quantitative accuracyImage enhancementImage analysisVeinVenous vessel
The invention provides a fundus retinal vessel recognition and quantification method, device and equipment and a storage medium, and the method comprises the steps: inputting an original fundus imageinto a pre-trained U-shaped convolutional neural network model for processing, and obtaining a target feature map; performing optic disk segmentation based on the target feature map; segmenting the original fundus image to obtain an arteriovenous blood vessel recognition result; carrying out region-of-interest positioning based on the optic disk segmentation result; extracting a blood vessel center line according to the arteriovenous blood vessel recognition result, detecting key points in the blood vessel center line, removing the key points to obtain a plurality of mutually independent bloodvessel sections, and correcting arteriovenous category information on each blood vessel section; and based on the extracted blood vessel center line, obtaining the blood vessel diameter of each bloodvessel section after category information correction, and then quantifying arteriovenous blood vessels in the region of interest. According to the embodiment of the invention, the fundus retina artery and vein vessel identification precision is improved, and the quantization precision is further improved.
Owner:PING AN TECH (SHENZHEN) CO LTD
A retinal fundus image segmentation method based on a deep full convolutional neural network
ActiveCN109598733AGuaranteed Segmentation AccuracyReduce distractionsImage enhancementImage analysisAutomatic segmentationEllipse
The invention discloses a retinal fundus image segmentation method based on a deep full convolutional neural network, and the method comprises the steps: selecting a training set and a test set, carrying out the extraction of a retinal fundus image, obtaining an optic disc positioning area image, and carrying out the blood vessel removal operation; Constructing a deep full convolutional neural network, taking the image of the optic disc positioning area as the input of the deep full convolutional neural network, performing optic disc segmentation model training on the training set based on thetrained weight parameter as an initial value to finely adjust model parameters, and performing parameter fine adjustment on the optic disc segmentation model on the basis; Using the trained visual cup segmentation model to perform visual cup and visual disc segmentation on the test set, performing ellipse fitting on a final segmentation result, and calculating a vertical cup-visual disc ratio according to a segmentation boundary of the visual cup and the visual disc. Automatic segmentation of the optic disc and the optic cup of the retinal fundus image is realized, the precision is high, andthe speed is high.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
An eye fundus image cup-disc segmentation method based on generative adversarial mechanism
ActiveCN109166095AIncrease contrastQuick checkImage enhancementImage analysisPattern recognitionDiscriminator
The invention discloses an eye fundus image cup-disc segmentation method based on a generative adversarial mechanism. The method includes the steps that data enhancement is carried out on a single-channel or multi-channel color eye fundus image, the fundus image is segmented through a U-Net network, the predictive segmentation image will be sent to the discriminator network to identify the true and false, the true and false judgment loss returns back to adjust a model generated by the U-Net network, after many times of running of a generative adversarial network, and finally the optimal opticdisc segmentation model and optic cup segmentation model are obtained. The method achieves the optic disc and optic cup segmentation detection.
Owner:GUANGDONG POLYTECHNIC NORMAL UNIV
Glaucoma optic disc segmentation map obtaining method
ActiveCN109919938AImprove reliabilityGood effectImage analysisEye diagnosticsData setOptic disc segmentation
The invention discloses a glaucoma optic disc segmentation map obtaining method. The method comprises the steps of acquiring and classifying a known color fundus image and a fundus image data set; constructing an optic disc preliminary segmentation deep learning network, and training, testing and correcting the network by utilizing the classification data to obtain an optic disc segmentation model; carrying out segmentation and screenshot on the test data set and the training data set; processing the screenshot; constructing an interpretable glaucoma preliminary auxiliary network; utilizing the screenshot to train and correct the interpreted glaucoma preliminary auxiliary network to obtain an interpretable glaucoma auxiliary network; segmenting the to-be-analyzed color fundus image data byusing an optic disc segmentation model to obtain an optic disc screenshot; processing the view disk screenshot by using an interpretable glaucoma auxiliary network to obtain a gray scale thermodynamic diagram; And processing the gray scale thermodynamic diagram to obtain a final view disk segmentation map. According to the method, the optic disc segmentation map for auxiliary diagnosis can be rapidly provided for doctors, and the method is high in reliability and good in effect.
Owner:CENT SOUTH UNIV
MCASPP neural network fundus image optic cup and optic disk segmentation model based on Attention mechanism
ActiveCN110610480AAvoid technical problems with lower precisionImprove feature extraction accuracyImage enhancementImage analysisFeature extractionPrediction probability
The embodiment of the invention relates to an MCASPP neural network fundus image optic cup and optic disc segmentation model based on an Attention mechanism. The model comprises a model body, the system comprises a feature extraction module, an attention mapping module, a multi-scale cavity convolution module and an output module. Extracting a first image feature in the input image through a feature extraction module; the attention mapping module is used for extracting a second image feature of the input image; and obtaining a first feature according to the advanced feature, the low-level feature and the second image feature in the first image feature, through the multi-scale cavity convolution module, the advanced features are subjected to multiple convolution to obtain the second features, and the output module obtains the prediction probability graph according to the first features and the second features, so that the feature extraction precision of the image segmentation network can be improved, and the technical problem of relatively low optic disc precision of optic cups segmented by a full convolution network in related technologies is avoided.
Owner:珠海全一科技有限公司
AMD grading system based on macular attention mechanism and uncertainty
PendingCN112446875AEnsure safetyEasy to useImage enhancementImage analysisOphthalmologyOptic disc segmentation
The invention provides an AMD grading system based on a macular attention mechanism and uncertainty, and relates to the technical field of macular disease detection. An optic disc segmentation image and a blood vessel segmentation image are obtained by using a segmentation network model, a macular area image is obtained according to the obtained optic disc segmentation image and the blood vessel segmentation image, a multichannel image is obtained by using an attention network sub-module, features are extracted by using a Bayesian deep learning classification network model, and through multiple times of dropout Monte Carlo, four groups of probability values and one group of noise corresponding to the four lesion types are output; and the classification network module gives out accidental uncertainty and model uncertainty while finally outputting a model classification result. And the safety performance of the model is ensured.
Owner:中科泰明(南京)科技有限公司
Fundus image optic disc segmentation method based on rapid mean shift
ActiveCN109872337AImprove accuracyEasy to calculateImage analysisCharacter and pattern recognitionImaging processingMean-shift
The invention discloses a fundus image optic disc segmentation method based on rapid mean shift, belongs to the technical field of image processing, and solves the problems of low accuracy, low robustness and low segmentation efficiency of an optic disc segmentation method in the prior art. The method comprises the steps of inputting and pre-processing an original fundus image wherein the originalfundus image is a to-be-analyzed fundus image; performing color correction on the fundus image subjected to color correction by using a multi-light-source color constancy algorithm; and carrying outoptic disc positioning by combining the blood vessel information in the preprocessed image, and segmenting out an optic disc region by utilizing rapid mean shift based on the fundus image after colorcorrection to obtain an optic disc image. The method is used for optic disc segmentation of the fundus image.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
FCM-based diabetic retina image optic disk segmentation method
PendingCN110889846AGood segmentation effectImage enhancementImage analysisDiabetic retinaImaging processing
The invention discloses an FCM diabetic retina image optic disk segmentation method, and belongs to the technical field of image processing. The method mainly comprises the following steps: step 1, acquiring a to-be-processed original fundus image R '(x, y), and carrying out image preprocessing on the image R' (x, y); step 2, carrying out blood vessel processing, including blood vessel segmentation and blood vessel erasing, on the preprocessed image R (x, y), the blood vessel segmentation mainly comprising the following parts: blood vessel enhancement, matched filtering, gray scale adjustmentand contour elimination; step 3, carrying out optic disk positioning on the image R after the blood vessel is erased; and step 4, carrying out optic disk segmentation by utilizing an FCM (fuzzy C-means) algorithm. The problems of large calculation amount, long consumed time, low accuracy and the like are solved by using the FCM algorithm, and the overall efficiency of the optic disk segmentation process is improved.
Owner:HARBIN UNIV OF SCI & TECH
Optic disc segmentation method with combination of fundus image edge information and brightness information
ActiveCN106530316AImprove efficiencyImprove positioning accuracyImage enhancementImage analysisVertical edgeOptic disc segmentation
The invention relates to an optic disc segmentation method with the combination of fundus image edge information and brightness information. The method comprises a step of carrying out preprocessing on a fundus image, a step of using an Isotropic Sobel operator to extract the vertical edge and horizontal edge images of the fundus image with enhanced contrast, and positioning the horizontal coordinate of an optic disc according to an edge difference curve, a step of extracting a vascular intensiveness characteristic and an optic disc brightness characteristic, a step of defining the product of vascular intensiveness characteristic and the optic disc brightness characteristic as a comprehensive characteristic value, obtaining a comprehensive characteristic value curve about the vertical coordinate, wherein the coordinate corresponding to a maximum value in the curve is a optic disc vertical coordinate, and a step of cutting the optic disc by using a CV horizontal set model according to the positioned optic disc center coordinate. The method has high segmentation efficiency.
Owner:TIANJIN UNIV
Automatic segmentation method for a visual disc and a visual cup of a color fundus image
ActiveCN109658423AAccurate automatic segmentationThe method is simple and reliableImage enhancementImage analysisAutomatic segmentationData set
The invention discloses an automatic segmentation method for an optic disc optic cup of a color fundus image. The method comprises the following steps: acquiring a known color fundus image and a corresponding fundus image data set; Constructing and obtaining an optic disc segmentation model; segmentating and intercepting The data set, and obtaining a screenshot; Constructing and obtaining an optic disc optic cup segmentation model under the Euclidean coordinates; Performing polar coordinate transformation on the screenshot to obtain a polar coordinate screenshot; Constructing and obtaining anoptic disc optic cup segmentation model under polar coordinates; Segmenting the to-be-analyzed data by using two types of models to obtain an Euclidean coordinate optic disc optic cup segmentation result and a polar coordinate optic disc optic cup segmentation result; And fusing the two types of segmentation results to obtain a final optic disc and optic cup segmentation result of the fundus image. According to the method, automatic optic disk and optic cup segmentation can be more accurately carried out on the color fundus image, and the method is simple, reliable and good in applicability.
Owner:CENT SOUTH UNIV
Hypertension risk prediction method, device, and equipment and medium
The invention relates to the field of medical science and technology, and provides a hypertension risk prediction method, device and equipment and a medium. The method comprises the following steps: obtaining the individual information and a fundus image of a to-be-detected target; respectively inputting the fundus image into a preset arteriovenous segmentation model, an optic disk segmentation model and a lesion recognition model, and respectively extracting arteriovenous blood vessels, optic disk regions and fundus lesion features in the fundus image; quantifying the diameters of arteriovenous blood vessels in the optic disc area to obtain blood vessel diameter quantized values corresponding to the arteriovenous blood vessels respectively; using a machine learning regression method to construct a risk prediction model for predicting the hypertension of the target to be detected based on the blood vessel diameter quantized value, the fundus lesion features and the individual information; and inputting the fundus image corresponding to the to-be-detected target into the risk prediction model for detection to obtain a hypertension prediction result of the to-be-detected target. The risk prediction model is trained by adopting a plurality of variable factors, so that the accuracy of hypertension risk prediction is greatly improved.
Owner:PING AN TECH (SHENZHEN) CO LTD
Optic cup and optic disk segmentation method based on fundus image data set transfer learning
ActiveCN112541923AImprove Segmentation AccuracySolve problems such as blurred boundariesImage enhancementImage analysisData setGlaucoma screening
The invention belongs to the technical field of artificial intelligence, and particularly relates to a medical fundus image data set, in particular to a optic cup and optic disk segmentation method for fundus image data set transfer learning. According to the method, through adversarial training of a backbone segmentation network and two domain discriminators, universal features between fundus image data sets are extracted, and the features are weighted by using an attention module, so that the problem of fuzzy optic disc boundary of the optic cup is solved, and the interference of other fundus lesions on a segmentation task is eliminated. On the premise that target data set labeling information is not used, the algorithm keeps high optic cup and optic disc segmentation precision in the fundus image data set migration process, and the limitation of insufficient labeled fundus data on the performance of a traditional automatic glaucoma screening method is effectively eliminated.
Owner:NANKAI UNIV
Model training method, cup-to-disk ratio determination method and device, equipment and storage medium
PendingCN112132265AGood segmentation resultImprove accuracyImage enhancementReconstruction from projectionMedicineOptic disc segmentation
The invention relates to the field of artificial intelligence, specifically uses a neural network, and discloses an optic cup and optic disc segmentation model training method, a cup-to-disc ratio determination method, device and equipment based on the neural network, and a storage medium, and the optic cup and optic disc segmentation model training method comprises the following steps: obtaininga sample image and an image label corresponding to the sample image; constructing sample data; inputting the sample data into a preset neural network to obtain a predicted optic cup and optic disc segmentation image; respectively projecting the image label and the predicted optic cup and optic disc segmentation image to obtain a label projection value corresponding to the image label and an imageprojection value of the predicted optic cup and optic disc segmentation image; respectively calculating the numerical value of the segmentation loss function and the numerical value of the projectionloss function to obtain the numerical value of the network loss function; and training the preset neural network according to the numerical value of the network loss function to obtain an optic cup and optic disc segmentation model. The invention is applicable to the field of smart medical treatment.
Owner:PING AN TECH (SHENZHEN) CO LTD
Retina optic disc segmentation method combining U-Net and region growing PCNN
PendingCN111815563AReduce distractionsGood segmentation effectImage enhancementImage analysisComputer hardwareData set
The invention discloses a U-Net and region growing PCNN combined retinal optic disk segmentation method. The method comprises the steps of performing graying processing on a retinal optic disk data set picture; performing CLAHE processing on the data set picture after graying processing to enhance the contrast between the optic disc and the background in the retinal optic disc image; partitioningthe retinal optic disc image; constructing and training a U-Net neural network model and roughly extracting pictures; constructing a regional growth PCNN neural network model; and carrying out retinaloptic disc segmentation by using the region growing PCNN. On one hand, the invention provides an improved U-Net retina optic disc image rough extraction method, and through the rough extraction, thebackground is significantly inhibited, the optic disc area is highlighted, and the picture contrast is increased, so that the picture quality of a data set is improved; on the other hand, the invention provides an optic disk image segmentation method based on the improved region growing PCNN, the PCNN segmentation performance is improved by changing a seed selection mode, a PCNN initial ignition threshold selection mode and a region growing end condition, and the segmentation of the complete optic disk is realized.
Owner:CHINA THREE GORGES UNIV
Optic cup and optic disk segmentation method based on rich context network
ActiveCN112884788AImprove segmentationSolve the problem that the segmentation is not smooth enoughImage enhancementImage analysisData setMedicine
The invention discloses an optic cup and optic disk segmentation method based on a rich context network. The method comprises the following steps: acquiring and processing existing color eye fundus image data to obtain a training data set; constructing an original optic cup and optic disc segmentation model, and training to obtain an optic cup and optic disc segmentation model; and segmenting the target color eye fundus image by using the optic cup and optic disc segmentation model to obtain a final optic cup and optic disc segmentation result. The invention further discloses an imaging method adopting the optic cup and optic disk segmentation method based on the rich context network. The invention provides a segmentation structure which is based on a convolutional neural network and can obtain sufficient context information to carry out optic disc and optic cup segmentation. Therefore, the method can improve the segmentation performance of the optic disc optic cup, solves the problem that the edge segmentation of the optic cup is not smooth enough, and is high in precision, good in reliability and good in segmentation effect.
Owner:CENT SOUTH UNIV
Eye fundus image optic cup and optic disc segmentation method under unified framework
PendingCN113870270AMake the most of inner relationshipsAccurate segmentationImage enhancementImage analysisEye SurgeonFeature extraction
The invention discloses an eye fundus image optic cup and optic disk segmentation method under a unified framework, and the method comprises the steps: obtaining an eye fundus image before segmentation, and carrying out the image preprocessing operation, such as cutting and rotating; generating a corresponding mask image according to an optic cup and optic disk area marked on the fundus color photo by an ophthalmologist; constructing a deep network for segmenting an optic cup and an optic disk; iteratively training the deep segmentation network by using the mask image and the fundus image, and optimizing network parameters; and segmenting the optic cup and the optic disc, and obtaining the segmentation results of the optic cup and the optic disc by using the trained segmentation network model. The invention provides a deep neural network for optic cup and optic disc segmentation. The deep neural network comprises a multi-scale feature extractor, a multi-scale feature transition and an attention pyramid structure. According to the method, the optic cup and the optic disc can be effectively segmented, the segmentation precision is high, and meanwhile, a new thought is provided for segmentation of fundus images and segmentation of other medical images.
Owner:BEIJING UNIV OF TECH
A Method of Optic Cup and Disc Segmentation Based on Fundus Map Dataset Transfer Learning
ActiveCN112541923BImprove Segmentation AccuracySolve problems such as blurred boundariesImage enhancementImage analysisData setGlaucoma screening
The invention belongs to the technical field of artificial intelligence, and in particular relates to a medical fundus map data set, in particular to an optic cup and optic disc segmentation method for transfer learning of the fundus map data set. Through the backbone segmentation network and the confrontation training of two domain discriminators, the method extracts the common features between the fundus image datasets, and uses the attention module to weight the features, which solves the problem of blurring the boundaries of the optic cup and disc, and excludes the rest. The interference of fundus lesions on segmentation tasks. On the premise of not using the labeling information of the target dataset, the algorithm maintains a high cup-optic disc segmentation accuracy during the transfer of the fundus map dataset, which effectively solves the limitation of the performance of traditional automatic glaucoma screening methods caused by insufficient fundus labeling data.
Owner:NANKAI UNIV
Image processing method and device, computer readable storage medium and electronic equipment
The invention provides an image processing method and device, a computer readable storage medium and electronic equipment, and relates to the technical field of image processing. The image processing method comprises the following steps: determining optic disc position information corresponding to a fundus image to be processed based on a preset target detection algorithm; determining an optic disc segmentation image and optic disc boundary information corresponding to the fundus image to be processed based on the optic disc position information and a preset edge detection algorithm; and determining an optic cup segmentation image and optic cup boundary information corresponding to the fundus image to be processed based on the optic disc position information. According to the image processing method provided by the embodiment of the invention, the eye fundus image optic disk and optic cup identification and extraction can be realized, and the segmentation precision reaches a sub-pixel level or above.
Owner:依未科技(北京)有限公司
Fundus image optic cup optic disc segmentation method and system for assisting glaucoma screening
ActiveCN110992382BBoost backpropagationImprove Segmentation AccuracyImage enhancementImage analysisInformation processingOptic disc segmentation
Owner:SICHUAN UNIV
A cup-and-disk segmentation method for fundus images based on generative adversarial mechanism
ActiveCN109166095BIncrease contrastQuick checkImage enhancementImage analysisPattern recognitionNetwork generation
The invention discloses a cup-and-disc segmentation method for fundus images based on a generative confrontation mechanism. By performing data enhancement processing on single-channel or multi-channel color fundus images, and then using U-Net network to segment the fundus images, the prediction obtained The segmented image will be sent to the discriminator network for authenticity identification, and the obtained authenticity judgment loss will be returned to adjust the model generated by the U-Net network. After multiple runs of the generation confrontation network, the optimal optic disc segmentation model and optic cup will be finally obtained. Segment the model to realize the segmentation detection of optic disc and optic cup.
Owner:GUANGDONG POLYTECHNIC NORMAL UNIV
Optic Disc Segmentation Algorithm Based on Edge Information and Luminance Information of Fundus Image
ActiveCN106530316BImprove efficiencyImprove positioning accuracyImage enhancementImage analysisVertical edgeOptic disc segmentation
The invention relates to a optic disc segmentation method that integrates fundus image edge information and brightness information, which includes: preprocessing the fundus image; using the Isotropic Sobel operator to extract the vertical edge and horizontal edge images of the contrast-enhanced fundus image, and based on the edge difference Curve, locate the horizontal coordinates of the optic disc; extract the blood vessel density feature and the optic disc brightness feature; define the product of the blood vessel density feature and the optic disc brightness feature as the comprehensive feature value of the vertical coordinate, and obtain the comprehensive feature value curve about the vertical coordinate, with the maximum on the curve The coordinates corresponding to the value are the vertical coordinates of the optic disc; based on the positioning of the optic disc center coordinates, the CV level set model is used to segment the optic disc. The invention has the characteristics of high segmentation efficiency.
Owner:TIANJIN UNIV
Mcaspp Neural Network Fundus Image Cup and Disc Segmentation Model Based on Attention Mechanism
ActiveCN110610480BAvoid technical problems with lower precisionImprove feature extraction accuracyImage enhancementImage analysisFeature extractionOptic disc segmentation
Examples of the present invention involve a McASPP neural network eye -looking cup visual disk segmentation model based on the Attention mechanism.Among them, this model includes: feature extraction module, attention mapping module, multi -scale empty convolutional module, and output module. The first image feature of the input image is extracted through the feature extraction module.The second image features, and the first feature of the high -level characteristics, low -level characteristics, and the second image characteristics in the first image characteristics. Through multi -scale empty convolutional modules, the second features are obtained by multiple convolution of high -level features. The output module is output module.According to the first and second features, the predictive probability chart can improve the characteristics of the characteristics of the image segmentation network and avoid technical problems with lower accuracy of the visual cup with full convolutional network segmentation in related technologies.
Owner:珠海全一科技有限公司
A Diabetic Retinopathy Detection System Based on Sequential Structural Segmentation
ActiveCN109472781BReduce workloadAccurate detectionImage enhancementImage analysisDiabetes retinopathyOptic disc segmentation
The invention discloses a diabetic retinopathy detection system based on serial structure segmentation, comprising: a fundus image acquisition device for acquiring retinal fundus images and a data processing device for analyzing and processing the collected fundus images, and the data processing device includes: a preprocessing function Module, blood vessel segmentation function module, optic disc segmentation function module, fovea determination function module, exudation segmentation function module, statistical calculation function module and doctor diagnosis function module, count the exudation area and calculate the presence of diabetic macular edema in the input fundus image Finally, combined with the results of statistical calculations, the fundus doctor will give the final diagnosis and treatment plan by referring to the segmented exudate area and disease probability, combined with his own specialty. The present invention systematically considers various related physiological structures of the fundus, segments out the lesion area and then gives a diagnosis report by the fundus doctor, which is efficient in detection and more accurate in detecting the lesion, which can greatly reduce the workload of doctors and improve work efficiency.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Visual cup and optic disc segmentation method based on depth level set learning
PendingCN114219814AImprove accuracySmooth borderImage enhancementImage analysisPattern recognitionOptic disc segmentation
The invention discloses an optic cup and optic disk segmentation method based on depth level set learning, comprising the following steps: S1, inputting an eye fundus image into a U-shaped neural network, the U-shaped neural network outputting a level set function value of each pixel in the image, a corresponding zero level set being a contour of a target object; s2, using a level set loss function to calculate an error between the output of the U-shaped neural network and a real label and update parameters of the U-shaped neural network, and introducing a contour length constraint and a region consistency constraint into the loss function, so that the neural network can obtain a level set meeting priori knowledge; and S3, training the two U-shaped neural networks to segment the optic disc and the optic cup respectively. The high-precision optic cup and optic disc segmentation method is realized, and the method can be effectively applied to automatic diagnosis of retinal diseases.
Owner:SOUTH CHINA UNIV OF TECH
BMO position positioning method of oct image
ActiveCN109744996BSolve the shortcomings of insufficient precisionGood precisionDiagnostic recording/measuringEye diagnosticsProjection imageOptic disc segmentation
The invention discloses a BMO position positioning method of an OCT image, comprising synthesizing a two-dimensional projection image and performing optic disc segmentation on a color fundus image; registering the color fundus image and the two-dimensional projection image to obtain the optic disc outline on the two-dimensional projection image; The optic disc outline is projected onto the OCT image to obtain two projection lines; the RPE layer is segmented and the rough positioning point of the BMO point is obtained; the recognition network is trained; the region of interest is extracted centered on the rough positioning point of the BMO point and input into the recognition network; the recognition result is Perform post-processing and select the image block with the best consistency as the final BMO positioning area; the center of the final BMO positioning area is the final BMO positioning point. The method of the present invention is superior to the existing methods in the accuracy of BMO positioning, and is closer to the result of manual calibration by experts, and the present invention can reduce the influence of the surrounding tissues of the BMO on automatic positioning, and help clinicians automatically calibrate the BMO position.
Owner:CENT SOUTH UNIV
Optic Cup Segmentation Method and Imaging Method Based on Rich Context Network
ActiveCN112884788BImprove segmentationSolve the problem that the segmentation is not smooth enoughImage enhancementImage analysisData setOptic disc segmentation
The invention discloses a method for segmenting the optic cup and optic disc based on a rich context network, which includes acquiring existing color fundus image data and processing to obtain a training data set; constructing an original model for optic cup and optic disc segmentation and training to obtain an optic cup and optic disc segmentation model; adopting The cup-optic-disc segmentation model segmented the target color fundus image to obtain the final cup-optic-disc segmentation result. The invention also discloses an imaging method using the optic cup and optic disc segmentation method based on the rich context network. The present invention proposes a segmentation structure based on a convolutional neural network and capable of obtaining sufficient context information for optic disc and cup segmentation; therefore, the method of the present invention can improve the segmentation performance of the optic disc and cup, and solve the problem that the segmentation of the optic cup edge is not smooth enough, Moreover, the accuracy is high, the reliability is good, and the segmentation effect is good.
Owner:CENT SOUTH UNIV
Fundus optic disc segmentation method based on U-Net neural network
The invention provides a fundus optic disc segmentation method based on a U-Net neural network. The fundus optic disc segmentation method based on the U-Net neural network comprises the following steps: collecting a plurality of original fundus images; preprocessing the plurality of original eye fundus images; marking optic disc areas of the plurality of preprocessed original eye fundus images; constructing a U-Net neural network, and performing training verification on the original fundus image and the fundus image marked with the optic disk area by using the U-Net neural network to obtain a U-Net neural network model; and using the U-Net neural network model to identify and segment the optic disc region of the fundus image. The method can assist a doctor in obtaining an accurate and effective cup-to-disk ratio, assists the doctor in making a treatment method, accelerates the speed of distinguishing the degree of glaucoma of a patient, and reduces misdiagnosis caused by subjective factors of the doctor.
Owner:FOSHAN UNIVERSITY
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