214 results about "Retinal image" patented technology

The invention discloses a diabetes retina image classification method and system based on deep learning, and relates to the technical field of image classification, and the specific steps are as follows: collecting and preprocessing a retina image data set, and constructing a training data set; constructing an initial image classification model based on a deep neural network, and performing iterative training on the initial image classification model by using the training data set to obtain an image classification model; the initial image classification model takes a pre-trained OfficientNet network as a backbone network, and integrates a feature pyramid network structure, a lesion attention module and a classification module; and obtaining a to-be-classified image, preprocessing the to-be-classified image, inputting the preprocessed to-be-classified image into the image classification model, and outputting a classification result. According to the method, the dependence of the model on a specific data source is reduced by using a data enhancement strategy, the diagnostic performance and stability of the model on unseen retina images are improved, and the universality of clinical application is enhanced.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

The invention discloses a retina thickness prediction method and system based on a multi-modal image, and the method and system achieve the effective estimation of the retina thickness under a low-cost condition through feature alignment and fusion modeling, and improve the basic screening and follow-up visit capability. According to the invention, through fusion of the multi-mode retina image data, the structure and function information of the optic nerve can be more comprehensively obtained, and the prediction accuracy of the thickness of the retina nerve fiber layer (RNFL) is improved. The OCT high-resolution hierarchical structure and the wide-view texture features of the eye fundus image are combined, so that anatomy and pathological states of optic nerves can be truly restored. The method can be used as an auxiliary method for early screening and early warning of optic neurodegenerative diseases such as glaucoma, and provides support for low-cost and high-efficiency primary screening and clinical auxiliary decision making.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The invention provides a method and a system for predicting an Alport syndrome through a multi-modal retina image based on deep learning, and relates to the technical field of deep learning. According to the method, a retina image, an OCT image and clinical text data are fused, a dual-channel deep neural network of an image encoder and a text encoder is constructed, semantic alignment pre-training is carried out by adopting multi-modal contrast learning, and an adversarial sample enhancement mechanism is introduced to improve model robustness; unified standardized training of different device images is achieved through the style migration module, the occurrence probability and the renal function progress risk level are finally output, and high-reliability prediction under the cross-modal and cross-device condition is achieved.

A method and system for the use of multi-spectral retinal images to achieve an effective, efficient, and AI enabled automated retinal disease biomarker detection using biomarker identification, segmentation, and quantification. A plurality of digital multi-spectral images of a retina of a patient at a plurality of illumination wavelengths can be done using a multi-spectral ophthalmoscope. The images can then be registered, processed, and assessed to automatically quantifying one or more biomarker based on the location and size of the biomarker in the plurality of processed multi-spectral images.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

The invention discloses a prediabetic prediction method based on tongue image-retina image bimodal feature fusion. The method comprises the steps of constructing a tongue image and retina image pair data set and performing preprocessing; constructing a pre-diabetes prediction model, wherein the pre-diabetes prediction model comprises two multi-scale Mamba modules with the same structure, a bimodal feature fusion module and a classifier; the multi-scale Mama module extracts hierarchical multi-scale semantic features of the tongue image and the retina image of the same patient, the dual-mode feature fusion module is used for splicing and fusion, and the fused features are input into the classifier to output a prediction result of the patient in the early stage of diabetes; training the model through the training set, and monitoring convergence by using the test set to obtain a trained model; and inputting the verification set into the trained model to obtain a pre-diabetes prediction result. The problems that in the prior art, the single medical image feature prediction rate is not high, meanwhile, the convolutional neural network is difficult to capture the long-distance dependency relationship, and the calculation cost based on Transform is high are solved.

The present invention provides a method for automatic segmentation of fundus blood vessels based on low-cost noise data, including: S1: screening the data set, obtaining retinal images and pixel-level blood vessel labels through publicly available data sets; S2: constructing a convolutional neural network to generate quantifiable noise data; S3: segmenting the data set to construct a training set and a test set, and at the same time retaining one clean image data in the training set and applying corresponding noise to other images; S4: setting a loss function and training parameters; S5: alternately inputting the clean image data and the noise data into the network, and updating the network parameters by using a reweighting function; S6: obtaining a retinal image and inputting it into the trained network model to output a segmentation result. The present invention realizes that only a small amount of clean data can correct the bias brought by incorrect noise to the deep model, and improves the accuracy of automatic segmentation of fundus blood vessels when facing low-cost noise data.

The invention discloses a diabetic nephropathy patient retinopathy image classification method based on artificial intelligence, and relates to the technical field of medical images, and the method comprises the steps: collecting a retina image, carrying out the denoising and contrast adjustment, and generating a standardized image; carrying out feature constraint by utilizing a generative adversarial network guided by medical priori knowledge in combination with microaneurysm morphology and bleeding area texture features, generating a retina image with enhanced pathological features, and constructing an amplification training data set; training a deep convolutional neural network based on the data set, identifying microaneurysm and bleeding lesion by using a multi-scale feature extraction layer, and outputting a preliminary classification result; and a reinforcement learning environment is constructed in combination with clinician feedback, and a classification decision threshold is dynamically adjusted to optimize lesion grading parameters. According to the method, cross-modal correlation analysis is adopted, retinopathy features and biochemical time sequence data are fused, and the accurate classification capacity of diabetic retinopathy images is improved.

An apparatus for obtaining an image of a retina is described herein. The apparatus includes an optical relay that defines an optical path and is configured to relay an image of the iris along the optical path to a pupil, a shutter is disposed at the pupil and configured to define at least a first shutter aperture for control of light transmission through the pupil position, a tube lens disposed to direct light from the shutter aperture to an image sensor, and a prismatic input port disposed between the shutter and the tube lens and configured to combine, onto the optical path, light from the relay with light conveyed along a second light path that is orthogonal to the optical path.

The invention discloses a retina OCT (Optical Coherence Tomography) image denoising method based on zero sample learning. The method comprises the following steps: generating a noise independent image pair for a single noisy image by adopting a CDIS (Coherent Discrete Identifier) The method comprises the following steps of: carrying out de-noising by using an MLFSnet network (AF-MSDSConv, Aap-LSM, SA, a reconstruction module); using RMSE symmetry and consistency joint loss zero sample training; in the reasoning stage, an original image is directly input, and a same-resolution de-noising result is output. The speckle noise can be significantly suppressed and the layered structure of the retina can be retained without noise-free true values or pairwise data, so that the retina can be used while being shot.

A method and system for the use of multi-spectral retinal images to achieve an effective, efficient, and AI enabled automated retinal disease biomarker detection using biomarker identification, segmentation, and quantification. A plurality of digital multi-spectral images of a retina of a patient at a plurality of illumination wavelengths can be done using a multi-spectral ophthalmoscope. The images can then be registered, processed, and assessed to automatically quantifying one or more biomarker based on the location and size of the biomarker in the plurality of processed multi-spectral images.

The invention relates to the technical field of medical health, and discloses a health assessment system and method based on multi-dimensional retinal aortic aneurysm monitoring data, and the method comprises the steps: collecting a retinal image of a patient with retinal aortic aneurysm, and extracting the aortic aneurysm features of the patient with retinal aortic aneurysm, setting an eye dynamic monitoring network of the patient with the greater aneurysm of the retina; performing time synchronization processing on the iconography image, the clinical examination data, the biomarker data and the real-time eye data to obtain multi-modal synchronization data; based on the multi-modal synchronous data, determining a risk evaluation index of the patient with the greater retina aneurysm, and constructing a personalized risk analysis model of the patient with the greater retina aneurysm; creating a risk scoring mechanism for the patient with the greater retinal aneurysm, and setting a health feedback tool for the patient with the greater retinal aneurysm. According to the invention, accurate prediction of the retina great aneurysm can be realized.

The invention discloses a retinal vessel segmentation and lesion detection method based on incremental learning. The method comprises the specific steps that firstly, an original image of a data set STARE is acquired, and preprocessing such as segmentation and data enhancement is carried out on an original retina image; then, a VGAT-Net-IL network model is constructed, the network takes a coding-decoding symmetric structure as a trunk, a visual cortex mechanism is simulated through an adaptive receptive field module to dynamically adjust a receptive field, and local details and global features of the retinal vessels are cooperatively extracted; meanwhile, a dynamic bimodal attention module is innovatively integrated, variable convolution is introduced into the dynamic bimodal attention module to adaptively adjust a sampling position, a blood vessel region is precisely focused in combination with a space and channel attention mechanism, and after the dynamic bimodal attention module, a Bayesian semantic association module is introduced to generate features containing semantic association; in order to solve the problem that old knowledge is easy to forget when a model learns new lesion features, an incremental learning technology training model is introduced. According to the method, the retinal vessel segmentation precision and the lesion detection capability are improved, and a reliable image analysis basis is provided for retinal disease diagnosis.

The invention relates to the technical field of image processing, in particular to a full-eye image automatic splicing method and system based on sweep-frequency OCT, and the method comprises the steps: collecting anterior segment images and retina images at different visual angles in combination with manual guidance; pre-processing the anterior segment images of different visual angles to obtain anterior chamber angle information, performing rigid registration on the anterior segment images based on the anterior chamber angle information, and fusing the registered anterior segment images to obtain an anterior segment OCT spliced image; performing transverse and longitudinal registration on the retina images of different visual angles by using the synchronously acquired laser eye fundus images, and performing fusion and curvature correction on the registered retina images to obtain a retina OCT spliced image; and splicing the anterior segment OCT spliced image and the retina OCT spliced image to obtain a full-eye image. According to the method and the device, the anterior segment image and the retina image can be seamlessly spliced while high-resolution detail presentation is ensured, and a more complete full-eye image with a clearer structure is obtained.

The present application discloses a method, apparatus, device, and storage medium for lesion grading, relating to the field of computer vision technology. The method includes: obtaining a retinal image to be processed; inputting the retinal image to be processed into a pre-trained diabetic retinopathy grading model to obtain a diabetic retinopathy grading result, where the diabetic retinopathy grading model is trained based on a multi-branch network architecture. The present application can improve the credibility of the diabetic retinopathy grading model.

Embodiments disclose systems and methods that aid in screening, diagnosis and / or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and / or integration with telemedicine services, programs, or software.

The embodiment of the invention provides a method for acquiring maculopathy edema features in a retina image and electronic equipment, and relates to the technical field of image recognition. The method for acquiring the maculopathy edema features in the retina images comprises the following steps: acquiring a current retina image to be recognized in a plurality of retina images; determining an edema brightness threshold value of the current retina image based on the brightness value of the pixel in the vitreous body region of the current retina image; based on the brightness value of each pixel in the current retina image and an edema brightness threshold value of the current retina image, an edema area in the current retina image is determined, and the brightness value of the pixel in the edema area is smaller than the edema brightness threshold value. According to the invention, feature extraction can be carried out on the macular oedema region in the image based on the brightness value of the pixel in the retina image.

Disclosed is an electronic device for screening autism spectrum disorder (ASD) and ASD symptom severity based on a retina image including an input unit that receives the retina image, a classification model that classifies whether there is the ASD, and the ASD symptom severity based on the retina image by using a deep learning algorithm, and at least one processor that controls the input unit and the classification model. The at least one processor is configured to preprocess the received retina image, to train the classification model such that the classification model classifies the ASD and typical development (TD) by using the preprocessed retina image, and classifies the ASD symptom severity, and to allow the trained classification model to screen whether there is the ASD and the ASD symptom severity depending on the input retina image.

The present application provides a retinal lesion segmentation method and device, electronic device and storage medium, which belong to the field of medical image processing technology. The method comprises the following steps: acquiring a retinal image, performing multi-stage encoding on the retinal image to obtain first-stage encoding features and second-stage encoding features, performing feature fusion on the first-stage encoding features and the second-stage encoding features to obtain target retinal encoding features, performing attention processing on the target retinal encoding features to obtain target retinal attention features, performing multi-stage decoding on the second-stage encoding features to obtain target stage decoding features, performing feature splicing on the target retinal attention features and the target stage decoding features from the channel dimension to obtain target retinal decoding features, performing lesion segmentation on the retinal image according to the target retinal decoding features, obtaining the retinal lesion category and retinal lesion location, and improving the accuracy of retinal lesion segmentation.

The invention relates to a system for creating planning data sets (34, 35, 36, 37) for the tissue treatment of the retina (16) of the eye (4) of a patient by means of a laser (2), with a camera (6, 7) which is configured to continuously capture images of the retina, with an image processing device (19) which is configured to generate and provide current structured image data of the retina from images captured by the camera, with a personal data acquisition device (21, 22), which is configured to acquire and / or provide personal data of a patient, and with a processing device (20), which is designed to assign planning datasets for the tissue treatment to the current structured image data of the retina and to the patient's personal data, wherein specifications, in particular patterns or rules, for the assignment of planning data to image data and patient's personal data are stored in the processing device.

The present application belongs to the field of medical image segmentation, and particularly relates to a retinal blood vessel image segmentation method. In view of the problems of low segmentation accuracy, insufficient segmentation ability of small blood vessels at the edge of eyeball, fracture at the blood vessel branch, and excessive interference of image noise in the existing retinal blood vessel image segmentation, the method comprises the steps of retinal image preprocessing and establishment of a retinal blood vessel segmentation model, wherein the preprocessing comprises converting a color retinal image into a gray image by giving different weights to the RGB three channels of the color retinal image; using a normalized and contrast-limited adaptive histogram equalization method to improve the image; using a local adaptive gamma change algorithm to adjust the retinal image; using translation, rotation, and noise increase to expand the data set; and the model establishment comprises feature extraction, feature fusion, and retinal blood vessel image segmentation.

The present invention discloses a fundus image segmentation method based on an uncertainty and shallow feature enhanced attention network, which comprises: selecting fundus retinal image data as a training set and a test set; preprocessing the fundus retinal images in the training set; constructing a fully convolutional neural network model on Pytorch; and segmenting the test set using the trained fully convolutional neural network model to obtain a final segmentation result. The present invention provides a neural network model for automatically segmenting retinal blood vessels from fundus images, which combines an attention mechanism to expand the receptive field so that the model can better understand the local details and global context in the image, and improves the learning ability of the model by fusing deep features in the neural network to guide the expression of shallow features. By quantifying image uncertainty, the adverse effects of the model's overconfidence on the segmentation results are reduced, thereby improving the accuracy of retinal blood vessel segmentation.

A patient screening system for providing recommendations for screening of a hospitalized patient for risk of developing deep vein thrombosis (DVT) or pulmonary embolism (PE) based on their health records and retinal images, is described herein. The patient screening system may include an optical imaging device for capturing retinal images, and a DVT risk assessment system configured to generate the recommendation for further screening tests. The DVT risk assessment system may implement various AI / ML models trained on a training dataset of anonymized patient data. The patient screening system may also implement detectors for various ophthalmic features correlated with blood clot-related conditions of the patient. Any patient screening based on the recommendation may be followed up, and results of such screening used to improve performance of the patient screening system.

The present disclosure is particularly directed to contact lens devices and / or methods for myopia treatment. The present disclosure is directed to modifying the incoming light by a contact lens, using a stop signal to slow the rate of progression of myopia. More particularly, the present disclosure is directed to a purposeful configuration of a non-circular, non-transparent aperture stop on the original base single-vision optic zone of a contact lens that can facilitate the redistribution of light energy into the oblique frequencies of the retinal image to provide an optical stop signal that prevents, reduces or controls the progression of refractive error of progressive myopia.

The invention provides a fundus retina image recognition method based on an improved lightweight network, and belongs to the technical field of image processing, and the method comprises the steps: obtaining pathological and non-pathological myopia fundus retina image data sets, carrying out the preprocessing and dividing, replacing an SE module with an ECA module based on MobileNetV3-Small, and optimizing the structure to obtain a lightweight model; during training, unbalance is processed by using an inverse frequency weighting method, and a recall rate optimal model is stored; and finally, the loading model carries out reasoning on the input image, and outputs and stores a recognition result. According to the fundus retina image recognition method based on the improved lightweight network, the problems that a traditional pathological myopia processing method depends on expert experience, diagnosis is difficult in a region lacking medical resources, and an existing model is large in parameter quantity, low in reasoning speed and difficult to deploy in a basic medical institution are solved.

A system, apparatus, and method of operation for focusing a retinal image are described herein. In an embodiment, the retinal imaging system comprises an eyepiece lens assembly; a structured light assembly configured to emit structured illumination light through the eyepiece lens assembly for receipt by an eye positioned adjacent to the eyepiece lens assembly and opposite the structured light assembly; a retinal image sensor optically coupled to the eyepiece lens assembly, wherein the retinal image sensor is positioned to receive the structured illumination light from the eye and is configured obtain a retinal image of a retina of the eye. In an embodiment, the method includes emitting structured illumination light through the eyepiece lens assembly; generating a retinal image based on the structured illumination light received from the retina of the eye; and measuring an edge strength of the structured illumination light from the retinal image.

The method and device for constructing a retinal atlas, computer device, and storage medium proposed in the embodiments of the present application obtain initial retinal images of different subjects, perform hierarchical processing on the initial retinal images to obtain an overall thickness image and multiple hierarchical thickness images, perform image registration on the overall thickness image to obtain a deformation vector field, perform image transformation on the hierarchical thickness images according to the deformation vector field to obtain target retinal images, combine the target retinal images corresponding to different subjects to obtain a retinal thickness image tensor, decompose the retinal thickness image tensor to obtain eigenvectors corresponding to each pixel point in the target retinal image, perform clustering on all pixel points in the target retinal image based on the eigenvectors to obtain a clustering result, and mark the pixel points corresponding to the target retinal image in the hierarchical thickness images according to the clustering result to obtain a target retinal atlas, which can improve the accuracy of retinal partitioning.

The invention discloses a diabetic retinopathy assessment method and system, and relates to the technical field of retina image detection.The method comprises the steps that a stepped pressure signal is generated through an intraocular pressure device, high-resolution OCT equipment is synchronously triggered, full-period response of blood vessels from compensation to decompensation is covered, and the limitation of traditional single-time pressurization detection is broken through; the change rate of the diameter of the blood vessel is calculated in real time, the pressure gradient or interval time is dynamically adjusted when the threshold value is exceeded, and the detection efficiency is improved while safety is guaranteed; constructing a stress-strain model of a pressure-diameter scatter diagram, extracting an initial elastic modulus, a hardening turning point and a hysteresis coefficient for joint analysis, and solving the problem of high misjudgment rate of a single parameter; and triggering a risk level signal when the at least two characteristic parameters exceed the limit. Through a technical closed loop of dynamic stimulation-image analysis-multi-parameter diagnosis, an innovative means is provided for early warning and accurate intervention of diabetic retinopathy.