26 results about "Retina eye" patented technology

The invention discloses an improved lightweight diabetic retina fundus image small target detection method based on RT-DETR. The detection method comprises the following steps: step 1, constructing a diabetic retina fundus image small target detection model; 2, obtaining a fundus image to be detected, and preprocessing the fundus image; 3, training and optimizing the detection model constructed in the step 1 by using a pre-training set, and inputting the to-be-detected eye fundus image preprocessed in the step 2 into the optimized detection model for detection reasoning; and step 4, outputting an eye fundus image with small target positions, classification and classification confidence. The invention further discloses a detection system for realizing the small target detection method and application of the small target detection method and the detection system, and the application prospect is wide.

The invention discloses a retina fundus image generation method based on a diffusion model. Firstly, a training data set and a regularization data set are constructed; then, constructing a diffusion model which comprises a variational auto-encoder, a U-Net denoising network and a text encoder; the variational auto-encoder comprises a variational encoder and a variational decoder, and the U-Net denoising network is embedded between the variational encoder and the variational decoder; the structured text prompt passes through a text encoder to obtain a text embedding vector, and the vector is injected into the U-Net denoising network as condition information; the variational encoder compresses the retina fundus image into potential features, the U-Net denoising network carries out denoising on the potential features under the guidance of condition information, and the denoised potential features are reconstructed into a high-resolution retina fundus image through the variational encoder; and finally, generating a retina fundus image based on the pre-trained diffusion model. The controllability of image generation is improved, and the generated image is highly consistent between the focus form and the medical description.

A system for detecting retinal detachment is provided herein. The system includes an imaging device configured to capture a retinal fundus image of an individual, a processor, and a memory storing instructions. When executed by the processor, the instructions cause the system to preprocess the retinal fundus image to remove extraneous background information and enhance retinal features, input the preprocessed retinal fundus image into a trained machine learning model, generate a determination of whether the retinal fundus image indicates presence of retinal detachment using the trained machine learning model, and output the determination to a user interface. The machine learning model is trained on a set of labeled retinal fundus images to classify images as indicating presence or absence of retinal detachment.

The invention provides an ophthalmology information generation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a target color retina fundus image to obtain fundus image features; performing feature extraction on the multi-task instruction text to obtain instruction text features; performing feature fusion according to the fundus image features and the instruction text features to obtain target image-text fusion features; obtaining a pre-trained multi-task prediction model; wherein in the training process of the multi-task prediction model, the sample eye fundus image and the multi-task label are used for training the multi-task prediction model; and performing multi-task prediction on the target image-text fusion feature through a multi-task prediction model to obtain target prediction information. In conclusion, the ophthalmology information generation accuracy can be improved, the information specialty and interpretability can be improved, and the ophthalmology information generation method can be safely and reliably applied to clinical practice.

The invention discloses a retina eye movement tracking and OCT (Optical Coherence Tomography) scanning compensation method and system, and the method comprises the steps: firstly dividing a fundus SLO image into a plurality of sub-regions, and calculating the stability index weight of each sub-region, thereby adaptively selecting a stable sub-region which is most suitable for being used as a tracking feature; and performing cross-correlation matching on the selected sub-regions to estimate local displacements, and performing weighted fusion on a plurality of local displacements based on a stability index weight to obtain a global eye motion vector of the current frame. According to the global eye motion vector, the drop point position of an OCT scanning light beam is adjusted in real time, and dynamic compensation of B-scan scanning is achieved. According to the method, stable and accurate retina positioning can be kept under the conditions of eye movement, illumination change and local image degradation, the alignment quality and the scanning definition of the OCT image are improved, automatic positioning of the same retina position can be achieved for examination of the same user at different moments, and the accuracy of retina positioning is improved. Regional repeated scanning can be directly carried out to observe focus changes at the same position, and the optical coherence tomography device is suitable for high-speed OCT imaging and clinical fundus examination.

In order to solve the problems of limited labeled data, differences between blood vessels and interference of lesion area, the present application discloses a retinal blood vessel image segmentation method based on a multi-scale dilated convolution residual network, which is still a challenge problem for accurately segmenting retinal blood vessels, especially fine blood vessels, on a retinal fundus image. The present application designs a multi-scale residual input and output module to make up for the loss of part of the blood vessel structure information due to down-sampling, combines the advantages of dilated convolution and DropBlock to relieve network overfitting and reduce the influence of the lesion area on blood vessel feature extraction, and further introduces a multi-scale mean pooling module to obtain high-level features and retain context information. Finally, by improving the way of jump connection, the dilated convolution is effectively used to improve the information transmission capacity of the jump connection. Compared with other algorithms, the present application can more accurately segment the fine blood vessels in the retinal image under complex conditions and has better robustness.

Described herein are techniques for imaging and / or measuring a subject's eye, including the subject's retina fundus. In some embodiments, one or more processors may be used to generate a graph from an image and / or measurement (e.g., an optical coherence tomography image and / or measurement), which can be useful for locating features in the image and / or measurement. For example, the image and / or measurement can include a subject's retina fundus and the features may include one or more layers and / or boundaries between layers of the subject's retina fundus in the image and / or measurement.

The application relates to a hard exudate segmentation method of a retinal fundus image, wherein a pretreated retinal fundus image is input into a pre-constructed and trained RMCA U-net model to obtain a hard exudate segmentation result; the RMCA U-net model comprises an encoder and a decoder; the encoder comprises multiple encoding stages, each encoding stage comprises a convolution layer, a DropBlock module, a maximum pooling layer and a double residual module which are sequentially connected, each decoder comprises multiple decoding stages, each decoding stage comprises a channel attention module, a deconvolution layer and a multi-scale feature fusion module which are sequentially connected, the channel attention module is used to utilize the most useful feature channel, and the multi-scale feature fusion module is used to guide the model to fuse multi-scale semantic information and global context features. Compared with the prior art, the application can accurately segment the fundus image under different field angles.

The present invention provides a method, device, and electronic device for calculating retinal vessel branching angles. The method comprises: obtaining a retinal fundus image and binarizing the image to obtain a black-and-white image; determining the optic disc center point based on the black-and-white image; and determining two target retinal branching vessels based on the black-and-white image; and determining the retinal vessel branching angles based on the optic disc center point and the two target branching vessels. The method, device, and electronic device for calculating retinal vessel branching angles provided by the present invention can accurately calculate the retinal vessel bifurcation angles of human eyes.

The present disclosure provides techniques and apparatus for capturing an image of a person's retina fundus, identifying the person, accessing various electronic records (including health records) or accounts or devices associated with the person, determining the person's predisposition to certain diseases, and / or diagnosing health issues of the person. Some embodiments provide imaging apparatus having one or more imaging devices for capturing one or more images of a person's eye(s). Imaging apparatus described herein may include electronics for analyzing and / or exchanging captured image and / or health data with other devices. In accordance with various embodiments, imaging apparatus described herein may be alternatively or additionally configured for biometric identification and / or health status determination techniques, as described herein.

The present application relates to the technical field of medical image segmentation, and particularly relates to a retinal fundus picture optic disc and cup adaptive segmentation method and system.The present application carries out multiple random forward propagation in the first U-Net to obtain a rough segmentation mean graph and a variance graph.Before calculating the second U-Net, the following adaptive judgment is made on the area of the rough segmentation optic disc: if the area is greater than or equal to a preset threshold, the fundus picture and the rough segmentation mean graph are input into the second U-Net to obtain the optic disc and cup segmentation result;if the area is less than the preset threshold, the weighted uncertainty graph needs to be further calculated, which is the weighted superposition of the rough segmentation optic disc and the high variance graph, and the weight considers both the distance of the rough segmentation optic disc and the high variance area and the shape of the optic disc and the high variance area.The weighted uncertainty graph, the fundus picture and the rough segmentation mean graph are input into the second U-Net to obtain the optic disc and cup segmentation result.Through adaptive judgment, the present application effectively improves the optic disc and cup segmentation precision.

The invention discloses a color retina fundus image enhancement method, which belongs to the technical field of medical image processing, and comprises the following steps: carrying out decomposition processing on an input color retina fundus image by using guide filtering, and separating a noise layer from a structural layer; further decomposing the structural layer into a base layer and a detail layer by using guide filtering again; performing color space conversion on the base layer, converting an RGB space into an HSV space, performing local brightness adaptive method adjustment on a V channel, and converting the HSV space after adjustment of the V channel into the RGB space; carrying out weighted enhancement processing on the detail layer based on a Weber coefficient; and fusing the enhanced detail layer and the base layer after brightness adjustment, and outputting an enhanced color retina fundus image. According to the invention, the color authenticity of the color retina fundus image can be maintained, and the local contrast and detail visibility of the image can be effectively improved.

An image processing system to improve the quality of images via a context-aware OT framework. The system combines a representation of each one of a set of unlabeled low-quality images and corresponding high-quality images as a collection of feature vectors in a deep layer of a neural network, then obtains contextual information, abstracted in the deep layer of the neural network, associated with the representation of each one of low-quality images and the high-quality images. The system then derives a context-aware optimal transport method based on the obtained contextual information, and trains the OT framework, during its training phase, to learn, via application of the context-aware optimal transport method, a mapping between each one of the low-quality images and high-quality images. Thereafter, a low-quality image can be translated, via the trained OT framework, during its inference phase, to a corresponding high-quality image.

The invention relates to the technical field of medical image segmentation, in particular to a retina fundus image optic disc optic cup self-adaptive segmentation method and a retina fundus image optic disc optic cup self-adaptive segmentation system. According to the invention, multiple random forward propagation is carried out on the first U-Net to obtain a coarse segmentation mean image and a variance image; before the second U-Net is calculated, performing the following adaptive judgment on the area of the roughly segmented optic disc: if the area is greater than or equal to a preset threshold value, inputting the fundus image and the roughly segmented mean image into the second U-Net to obtain an optic disc and optic cup segmentation result; if the area is smaller than a preset threshold value, a weighted uncertainty graph needs to be further calculated, the graph is weighted superposition of the roughly-segmented optic disc and the high-variance graph, and the weight considers the distance between the roughly-segmented optic disc and the high-variance region and also considers the shapes of the optic disc and the high-variance region. And inputting the weighted uncertainty map, the fundus image and the coarse segmentation mean image into a second U-Net to obtain an optic disc and optic cup segmentation result. Through adaptive judgment, the optic disc and optic cup segmentation precision is effectively improved.

The present invention discloses a method for jointly segmenting the optic cup and optic disc in retinal fundus images, belonging to the field of medical image processing. The present invention combines the elliptical morphological features of the optic cup and optic disc to construct a two-stage optic cup and optic disc joint segmentation network model from the perspective of ellipse detection. In the first stage, the optic cup is located within the optic disc region, and the Paired-Box RPN is introduced to achieve coupled detection of the minimum bounding rectangle of the optic cup and optic disc. In the second stage, the five parameters of the ellipse in the bounding box region are learned. Based on the elliptical morphological features and spatial geometric constraints of the optic cup and optic disc, the optic cup and optic disc are jointly segmented using the optic cup and optic disc joint segmentation network model. This method can solve the problem of uneven segmentation edges and achieve accurate optic cup and optic disc segmentation.

The present application relates to a retinal blood vessel segmentation method based on neural ordinary differential equation, which solves the defects of poor scale adaptability and insufficient extraction of fine blood vessel features compared with the prior art. The present application comprises the following steps: obtaining a retinal fundus image and pre-processing; constructing a retinal blood vessel segmentation network; training the retinal blood vessel segmentation network; obtaining the retinal blood vessel segmentation result of the fundus image. The encoder and decoder paths are stacked by the neural ordinary differential equation module, which uses its continuous dynamic characteristics to adaptively adjust the effective receptive field by calculating the feature derivative to adapt to different scale blood vessel features; the mixed attention perception module is located at the jump connection, which adopts the "asymmetric convolution decomposition" architecture, combines channel mixing and multi-scale spatial attention, and accurately captures fine linear blood vessel features while suppressing background noise.

The application provides a retinal fundus blood vessel segmentation method based on an LBP operator and a double-U network, and comprises the following steps: acquiring a public expert-annotated color fundus blood vessel segmentation dataset, which is divided into a training set, a verification set and a test set for network training and testing; performing data preprocessing on an original color fundus image, calculating an LBP code image on a grayscale image by using an LBP operator, and performing data augmentation by means of random slicing; constructing a double-U fundus blood vessel segmentation network with a double-branch residual decoding block, including a high-level semantic network taking the preprocessed fundus image as input, a shallow texture network taking the LBP code image as input, and a feature fusion module fusing high-level semantic features extracted by the high-level semantic network and fine-grained texture features extracted by the shallow texture network; obtaining an optimal segmentation model through multiple rounds of iteration; and verifying the segmentation effect and generalization performance of the model. The application improves the prediction ability of the semantic segmentation model and improves the generalization performance of the model.

An AI-based retinal image analysis system for the automated detection of diabetic retinopathy, comprising the following: an image acquisition unit consisting of an optical arrangement with a coaxial illumination source, a multi-lens arrangement and an image sensor for capturing retinal images; a preprocessing unit that is operationally coupled with the image acquisition unit and is configured to receive the retinal fundus images and perform image normalization including illumination correction, noise reduction and contrast enhancement; a feature extraction unit comprising at least one processor configured to perform a variety of folding operations on the preprocessed retinal fundus images to generate hierarchical feature representations corresponding to the anatomical structures and pathological regions of the retina; a classification unit comprising at least one processor configured to process the hierarchical feature representations and produce a severity classification output corresponding to the stages of diabetic retinopathy; a storage unit that is operationally coupled to the feature extraction unit and the classification unit and stores the trained parameters assigned to the feature extraction unit and the classification unit; and a display unit configured to show the user the severity classification output and the corresponding diagnostic information.

The application discloses an eye age difference index construction method and system based on fundus images and electronic equipment, and relates to the technical field of image processing. The application trains the constructed double-channel fusion attention network model by adopting a training set and a pairing matrix loss function, can reduce the prediction error between samples in addition to the mean square error, and makes the prediction accuracy of the trained double-channel fusion attention network model further improved. The trained network model is used as a fundus age prediction model to analyze the left and right eye retina fundus images of a to-be-measured object, and the characteristics are fused, so that more accurate fundus age can be obtained, so as to accurately construct the eye age difference index, and then provide a favorable basis for judging the aging track degree of human health.

The invention discloses a microaneurysm segmentation method based on grouping attention and multi-scale depth fusion, and belongs to the technical field of medical image processing. Comprises: obtaining a retina fundus image; the method comprises the following steps: constructing a network model based on packet attention and multi-scale deep fusion, taking a U-Net network as a basis, using ResNet50 as a backbone network of an encoder, embedding a packet multi-scale attention module GMA in a bottleneck block of the ResNet50, and introducing a multi-scale feature transfer module MSDF between the encoder and a decoder to optimize feature transfer; and inputting a to-be-segmented retina fundus image into the trained network model based on grouped attention and multi-scale depth fusion for segmentation to obtain a microaneurysm segmentation result. According to the method, grouping attention and multi-scale depth are fused, and the problems that an MA target is likely to be confused with the background, the edge is fuzzy, the proportion is small, and accurate segmentation is difficult are solved.

The application discloses a cataract severity stratification method and device based on artificial intelligence, computer equipment and a medium. The method comprises the following steps: acquiring a retinal image and performing pretreatment to obtain a pretreated retinal posterior segment image; performing feature extraction on the pretreated retinal posterior segment image, wherein the feature extraction comprises feature extraction based on an artificial intelligence model; and acquiring a disease type corresponding to a classification probability result based on the artificial intelligence model and the classification probability result.

Embodiments of the present application provide a method for obtaining a retinal topography map and an apparatus for diagnosing eye diseases. The method comprises: obtaining a retinal fundus image; inputting the retinal fundus image into a target image generator, and obtaining a retinal topography map via the target image generator; and providing the retinal topography map. The method of the embodiments of the present application can provide doctors with more retinal topography maps for diagnosing eye diseases in patients. The apparatus of the embodiments of the present application can employ software methods to generate topography maps that facilitate subsequent diagnosis by doctors in combination with other eye images. In other words, the topography maps obtained in some embodiments of the present application can be used to assist doctors in diagnosing and treating diseases.

The application discloses a kind of based on histogram matching and weighted retinal fundus image splicing fusion method, first the retinal fundus image of different regions of the same mode obtained is histogram matched, for reducing the brightness and contrast difference between image, then the weight matrix and affine matrix of image are sequentially obtained, and the histogram matched image and weight matrix are carried out affine transformation, finally based on the image and weight matrix after affine transformation sequentially carry out the splicing fusion of image.The scheme of the application, when histogram matching is carried out, the pixel distribution histogram of all images is counted as the target of histogram matching, reduces the brightness contrast difference between image, while avoiding the image distortion caused by using specific target;While using weight control the value of pixel when fusion image, using the way of segmentation carries out weighted summation, effectively reduces the difference at image joint, and the image fusion effect is good.

A method for treating a subject having a retinal eye condition that is refractory to anti-VEGF treatments is disclosed. The method comprises administering a therapeutically effective amount of one or more steroids subsequent to anti-VEGF treatment, thereby treating the retinal eye condition. Also disclosed is a method for treating a subject having a retinal eye condition that is refractory to anti-VEGF treatments, the method comprising a therapeutically effective amount of one or more compounds capable of modulating an activity of a steroid receptor, subsequent to anti-VEGF treatment. Further disclosed is a method for treating a subject having a retinal eye condition that is refractory to anti-VEGF treatments comprising administering a therapeutically acceptable formulation of a steroid, and at least a second therapeutically active compound in a concentration and dose sufficient to ameliorate the retinal eye condition, subsequent to anti-VEGF treatment.

This invention discloses a retinal vessel segmentation method based on orientation field guidance and graph neural network optimization, belonging to the field of image analysis and medical image processing technology. The invention first acquires retinal fundus images and their corresponding vessel annotation maps, and preprocesses the raw retinal fundus images. Then, it constructs a retinal vessel segmentation model based on orientation field guidance and graph neural network optimization. This model includes a backbone segmentation model based on a UNet++ network architecture and a post-processing refinement module based on a visual graph neural network. The preprocessed image is input into the backbone segmentation model to obtain a preliminary vessel segmentation probability map, which is then processed by the post-processing refinement module to obtain the final vessel segmentation result. Experiments show that this invention effectively addresses the shortcomings of existing retinal vessel segmentation methods in terms of small vessel extraction, structural connectivity preservation, and background noise suppression.

A method of estimating optical coherence tomography (OCT) parameters includes: obtaining a retinal fundus image; and segmenting the retina fundus image for generating a 2D image of the target region. The method further comprises the steps of inputting the 2D image of the target area into a neural network; estimated OCT parameters are generated using a neural network.