73 results about "Optic disc size" patented technology

The invention provides a dynamic screening medical image target tracking method and device based on visual Transform, and relates to the technical field of computer vision, and the method comprises the steps: standardizing near-infrared or visible light fundus video frames into uniform resolution, constructing a template-search frame pair, and then jointly mapping the two frames of images into a Token sequence; a dynamic local interaction module is embedded in front of each pruning layer of the whole network, a local context is captured by using depth separable convolution and point convolution, a dynamic convolution kernel generator is driven, and a neighborhood Token is adaptively weighted and aggregated. Next, the Token screening and the compression mechanism TSC are operated in the same pruning layer, only the Top-K key Token is reserved, the redundant Token is cut off, and the original index is recorded; the objective of the invention is to improve the positioning stability and reasoning efficiency of a focus area (such as an optic disc) in a complex operation video.

The invention discloses a self-adaption eye fundus image processing method and device based on a passive field. The method comprises the following steps: acquiring an eye fundus image with a label in a source domain and an eye fundus image without a label in a target domain, and preprocessing the eye fundus image; training a teacher model and optimizing model parameters; using teacher model parameters to initialize two student models with the same structure; inputting the target domain unmarked fundus image into the teacher model and the student model for pixel-level learning; constructing category prototypes of optic cups and optic disks based on high-confidence features extracted by the teacher model, and guiding student model features to be aligned with the category prototypes; calculating the prediction difference of the teacher model and the student model in the boundary region, and correcting the segmentation result of the fuzzy boundary; constructing a total loss function, and iteratively updating teacher model parameters to obtain a fundus image segmentation model; and performing optic cup and optic disk segmentation on the target domain eye fundus image by using the eye fundus image segmentation model, and performing quantitative analysis. According to the method, the generalization and segmentation performance of the model on the target domain is remarkably improved.

The invention discloses an optic disk atrophy arc image segmentation method based on full supervision, and particularly relates to the technical field of medical equipment, and the method comprises the following steps: S1, multi-scale feature extraction, S2, local-global feature enhancement, S3, adaptive feature fusion, S4, gradient flow optimization, S5, mixing of a loss function, and S6, data enhancement and generalization optimization. According to the method, the long-range dependence of the optic disk and the atrophic arc is globally modeled through the PVTv2 encoder, the detail segmentation precision of the optic disk atrophic arc at the blood vessel crossing and fuzzy boundary is remarkably improved in combination with the multi-scale attention and dilated convolution of the feature enhancement module, cross-level feature fusion is realized with linear complexity based on the Mama decoder, and the accuracy of feature fusion is improved. Efficiency and structure coherence are considered; the weight is balanced by the mixed loss function, and early lesion missing detection is reduced; a data enhancement and residual module enhances the generalization ability of the model, and a high-precision and efficient quantitative analysis tool is integrally provided for early screening of blind eye diseases such as pathological myopia and glaucoma.

A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and / or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and / or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.

The invention provides an optic disk real-time continuous tracking method and device, and relates to the technical field of medical image processing. According to the method, the video sequence of the ophthalmologic operation is acquired and preprocessed, and then the preprocessed video sequence is subjected to multi-scale feature extraction and optic disc target detection, so that an optic disc detection target is obtained; the optic disk detection target is based on Kalman filtering prediction and appearance feature matching, an adaptive gating matching mechanism is introduced to carry out inter-frame target trajectory association, a target trajectory is maintained in combination with an appearance feature cache updating mechanism and ReID cosine similarity retrieval, and a trajectory tracking result is output; wherein the trajectory tracking result comprises a continuous tracking result of the optic disc position, the confidence coefficient, the trajectory identifier and the timestamp. According to the invention, the detection precision of the small-scale optic disc is improved, the shielding recovery and track continuity are enhanced, and high-precision, high-robustness and real-time optic disc continuous tracking is realized in complex operation scenes of strong reflection, low contrast, local shielding and the like.

The invention relates to a diabetic retinopathy fundus photography grading report system combined with a clinical guide, based on an artificial intelligence deep learning technology, and belongs to the field of fundus lesion analysis. The system accurately identifies and segments various lesions such as microhemangioma, bleeding, exudation and the like and symbolic structures such as optic discs, macular regions and the like by automatically analyzing fundus photographic images. The system adopts ICDR international standards to grade diabetic retinopathy, and provides diagnosis and treatment suggestions for lesion characteristics in combination with clinical guidelines of American ophthalmology institute in 2019. Through big data training, the system can generate detailed reports in real time, the early screening rate is remarkably improved, misdiagnosis and missed diagnosis are reduced, and the diagnosis speed and accuracy are improved. The system comprises a plurality of modules, such as an image pre-classification module, a deep learning focus recognition module, an ICDR grading module and a report generation module, efficient and accurate diabetic retinopathy diagnosis and grading are cooperatively achieved, and the clinical management level is improved.

The invention discloses a fundus image diagnosis method based on a fusion attention mechanism, and the method comprises the following steps: obtaining a to-be-processed fundus image, and carrying out the normalization processing of the fundus image; performing optic disc segmentation on the fundus image through an encoder-decoder network Attention U-Net embedded with an attention mechanism, and generating a segmentation mask; inputting the segmented mask and the original fundus image into a dynamic weighted feature fusion module to generate a fused image; performing feature extraction on the fused image through a ResNet classification network, and outputting an eye disease classification result; wherein the segmentation and classification tasks are subjected to end-to-end training through joint optimization of a loss function.

The invention discloses a difficulty perception-based passive domain adaptive fundus image segmentation method and device, and belongs to the technical field of computer vision processing, and the method comprises the steps: obtaining a target domain fundus image, and carrying out the preprocessing of the target domain fundus image; initializing a teacher model and two student models; performing weak enhancement processing on the preprocessed image, inputting the processed image into a teacher model, obtaining a prediction probability, calculating an entropy value of each sample, and dividing the samples into an easy sample set and a difficult sample set according to the entropy values; strong enhancement processing is carried out on the easy sample set and the difficult sample set, and then the easy sample set and the difficult sample set are respectively input into student models for training; external information is injected into the two student models respectively, and the training process is optimized; after each iteration period is finished, the parameters of the teacher model are updated based on the parameters of the two student models until the teacher model converges; and inputting a to-be-segmented eye fundus image into the converged teacher model to obtain a segmentation result of the eye fundus image. According to the invention, high-precision segmentation of the optic cup and optic disc of the eye fundus image is realized.

The invention discloses a passive domain field adaptive eye fundus image segmentation method based on clustering filter pseudo label optimization. The method comprises the following steps: step 1, student network eye fundus optic cup and optic disc segmentation; 2, segmenting a teacher network fundus optic cup and an optic disk; step 3, clustering a filter; step 4, performing pseudo label optimization based on a clustering filter; 5, calculating a student network segmentation loss function and updating parameters; step 6, updating teacher network parameters; and step 7, calculating a weighted Dice loss function. According to the method, the false label generated by the teacher model is optimized, the stability of the false label is enhanced, a weighted Dice loss function is introduced, the model is forced to pay attention to a sparse but important target area, the deviation caused by class imbalance is reduced, and higher-precision fundus optic disc and optic cup segmentation is realized.

This application discloses a method, apparatus, device, storage medium, and product for optic disc segmentation. The method includes: extracting multi-level features from a fundus image at both local and global levels; generating several levels of local and global features in ascending order; and then fusing the features in descending order to obtain fused features, thereby obtaining the optic disc segmentation result based on the lowest-level fused features. Each level of feature fusion includes: fusing the local and global features of the current level to obtain preliminary fused features; filtering the preliminary fused features of the current level using a spatial attention module; weighting the local and global features of the current level using the filtered data; and then combining the two weighted features with the fused features output from the next higher level to generate the final fused features of the current level. This application can improve the segmentation accuracy of the optic disc.

This disclosure provides a measurement system and method for the pathological features of hypertensive retinopathy. The measurement system includes: an acquisition module that acquires fundus images; a partitioning module that receives the fundus images and identifies and partitions the optic disc region; a segmentation module that uses an arteriovenous segmentation model to segment the fundus images into arteries and veins to obtain a three-valued image of the segmentation results. The arteriovenous vessel marking results include arterial and venous marking results formed by marking the boundaries of vessels with diameters larger than a preset vessel diameter in the training fundus images, and small vessel marking results formed by marking the course of vessels with diameters not larger than the preset vessel diameter. The measurement module measures the pathological features of hypertensive retinopathy in the fundus images based on the vessels larger than the preset vessel diameter in the partitioning and segmentation results. This disclosure enables efficient and objective measurement of the characteristics of hypertensive retinopathy.

The invention relates to the technical field of medical image processing, and discloses a premature infant retinopathy diagnosis system based on an isotropic convolutional network, which comprises an image acquisition module, a segmentation module, a partition module, a staging module and a result generation module. The segmentation module is used for processing the retina image by adopting an equivariant convolutional network, and accurately segmenting an optic disc and a lesion area; the partitioning module divides a retina region according to an optic disc segmentation result; the staging module integrates the lesion segmentation result and the zoning result to output lesion staging; and the result generation module integrates all information to generate a comprehensive diagnosis report. According to the premature infant retinopathy diagnosis method and system, the image segmentation precision is improved through the equivariant convolutional network, and the automatic and high-accuracy comprehensive diagnosis of the premature infant retinopathy is realized by utilizing the cooperative work of the modules, so that the clinical diagnosis and treatment decision is effectively assisted.

The embodiment of the invention provides a fundus image segmentation method and related equipment, and belongs to the technical field of medical image processing. The method comprises the following steps: constructing and training a segmentation model which integrates a multi-scale U-Net main network, a deblurring diffusion module and a variational auto-encoder module; wherein the multi-scale U-Net enhances detail capture by introducing frequency domain reweighted jump connection; the variational auto-encoder module provides global structure prior of the image; and the deblurring diffusion module uses the prior as condition guidance to de-noise and enhance the features. According to the method, three advanced models are innovatively and synergistically fused, the problems that in the prior art, the segmentation precision of complex blood vessels and optic disc / optic cup structures in fundus images is insufficient, the robustness of blurred images is poor, and a large amount of labeled data is relied on are effectively solved, and the segmentation accuracy and stability are remarkably improved.

This invention pertains to ophthalmic image processing methods, specifically a method and system for ROI region segmentation to assist optic disc recognition. The method includes the following steps: acquiring a fundus image to be identified; identifying peripapillary atrophy arc segments in the fundus image to be identified, obtaining the identification results; determining a pre-selected region based on the intensity centroid of the fundus image to be identified; within the pre-selected region, determining the region of interest (ROI) using quadrilateral correction and the peripapillary atrophy arc segment identification results; and performing image recognition based on the ROI. This involves identifying the peripapillary atrophy arc segments and using target boundaries that intersect the peripapillary atrophy arc segments twice to perform translational correction on the fixed-size ROI, thereby utilizing peripapillary atrophy to locate the optic disc and improving the accuracy of the ROI's coverage of the actual optic disc area.

The invention discloses an ophthalmology intelligent auxiliary diagnosis method and system based on heterogeneous data collaboration, and the method comprises the steps: synchronously obtaining an ophthalmology multi-mode heterogeneous data set of a patient, including an OCT image, a fundus photograph and a visual field inspection report; the OCT image is subjected to retina layered structure segmentation, and an RNFL thickness distribution diagram is extracted; performing optic disc region positioning and blood vessel tree segmentation on the fundus photography to generate a CDR and a blood vessel curvature quantized value; mode deviation probability graph coding is carried out on the visual field inspection report, and a quadrant sensitivity missing matrix is output; an ophthalmic multimodal heterogeneous data set is associated through a patient unique identifier, and a RNFL thickness distribution map of OCT is mapped to an optic disc coordinate system of fundus photography by using feature-driven non-rigid registration. According to the invention, an ophthalmology multi-modal data cooperation barrier is broken through, an intelligent decision-making system with structure-function mutual verification is constructed, and the diagnosis reliability and clinical interpretability are significantly improved.

The invention discloses an optic disc and optic cup segmentation method based on semi-supervised learning, belongs to the field of image processing and artificial intelligence, and aims to solve the problems of instability of an optic disc and optic cup segmentation model caused by few labeled eye fundus images and poor precision caused by noise pollution. In the first stage, a double-branch improved DeepLab v3 + model is constructed to complete optic disc segmentation, after full supervision and anti-noise training are carried out by utilizing an annotated fundus image, the double-branch model is used for processing an unannotated fundus image, the anti-noise capability of the model is evaluated by counting the consistency of pseudo-label pixels before and after noise, and the model with strong anti-noise performance is used for supervising the training of the other model; in the second stage, a SegFormer model is constructed and trained to complete optic cup segmentation; and finally connecting the two models in series to realize two-stage segmentation of the optic disc and the optic cup. According to the method, unlabeled data is fully utilized for semi-supervised training, the noise immunity and segmentation precision of the model are improved, and technical support is provided for glaucoma screening work.

The invention provides a retinopathy auxiliary diagnosis system, and the system comprises an image enhancement module which carries out the defogging enhancement processing of an inputted fundus image set, and outputs an enhanced image set; the target detection module positions the optic disc and the macular of each fundus image in the enhanced image set, and outputs a positioning result; the orientation screening module judges and screens qualified images according to a positioning result and a predefined orientation rule; the extraction backbone of the double-branch classification network extracts high-level features and middle-level features; the first classification branch outputs a global classification result based on the high-level features; the second classification branch outputs a local classification result based on the middle-level features; and the diagnosis unit receives the global classification result and the local classification result to generate an auxiliary diagnosis result. According to the system, a discrete series model is replaced by an integrated architecture, redundant calculation is eliminated through connection of image enhancement and target detection, and the problem of high risk of missed diagnosis is solved through orientation evaluation, global and local branch parallel processing and collaborative optimization.

The invention provides an image processing method, electronic equipment and a storage medium, and the method comprises the steps: carrying out the gray processing of an eye fundus image, and obtaining a gray image, the retina in the eye fundus image comprises an optic disc; determining a relationship between a corresponding brightness value and a pixel point based on the grayscale image, and determining a target position of the optic disk; and determining a first classification result of the fundus image according to the target position, wherein the first classification result comprises a left eye image and a right eye image. The method can reduce the classification cost of the eye fundus image and improve the classification precision.

The invention discloses a calculation method for standardized statistics of the area of a retinal fibrous vascular membrane, and the method comprises the following steps: employing a same non-contact wide-angle imaging system and a same amplification factor, and taking an optic disc as a center to shoot a standard eye model reference image containing a preset model vitreous head and an eye fundus image in a patient eye operation; by taking a vitreous body cutting head as a reference, aligning the two images through Canny edge detection, minimum circumcircle positioning and Hough transform, and zooming until the contour of the vitreous body cutting head coincides; the method comprises the following steps: carrying out graying, de-noising, Otsu segmentation, connectivity screening and edge fitting on the basis of a white highlight feature of an FVM (Fiber Virtual Machine), so as to obtain an FVM contour; and overlapping the grids to count the number of the covered grids, and finally calculating the actual area of the FVM according to a formula. The invention provides an FVM area quantification calculation scheme which is uniform in reference standard, accurate in quantification and wide in application range, and provides a uniform quantification standard for comparing the FVM resection efficiency of different surgical instruments.

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.

A method of determining pathologic myopia by using the geometric structure of a posterior sclera of a fundus includes obtaining geometric location data about a fovea, an optic disc, and a deepest point of the eye (DPE) in the posterior sclera of the fundus; obtaining an elevation difference TEPSfovea−DPE between the fovea and the DPE, an elevation difference TEPSdisc−DPE between the optic disc and the DPE, and an elevation difference TEPSfovea−disc between the fovea and the optic disc by using location information about the fovea, the optic disc, and the DPE in the posterior sclera; and determining pathologic myopia by using at least two elevation differences selected from among the elevation difference TEPSfovea−DPE between the fovea and the DPE, the elevation difference TEPSdisc−DPE between the optic disc and the DPE, and the elevation difference TEPSfovea−disc between the fovea and the optic disc.

The disclosure describes a neural network-based glaucoma recognition device and method, which includes an input unit, a preprocessing unit, a segmentation unit, a feature extraction unit, and a classification unit. The input unit is used to receive a first image. The preprocessing unit is used to preprocess the first image to obtain a first preprocessed image. The segmentation unit is used to input the first preprocessed image into an artificial neural network based on deep learning to generate an optic disc region image and an optic cup region image. The feature extraction unit obtains multiple glaucoma features based on the optic disc region image and the optic cup region image corresponding to the first preprocessed image. The classification unit is used to input feature information including glaucoma features corresponding to the first preprocessed image into a classifier based on machine learning for classification to obtain a glaucoma classification result. According to the present scheme, the accuracy of glaucoma recognition can be improved.

This invention relates to the fields of medical image processing and auxiliary diagnosis of ophthalmic diseases, specifically to a method and system for retinal vessel and optic disc segmentation based on multi-dimensional attention and edge enhancement mechanisms. It first acquires and preprocesses fundus images, then constructs an EIAIU_Net segmentation model integrating a multi-dimensional attention module and an edge enhancement Transformer module. The model is trained based on a paired shuffling consistency strategy, which reduces the dependence on absolute positional information. After inputting the preprocessed image into the model, multi-path features are generated through residual fusion using the SSA-IAI module, and then bidirectional interaction between edge and global features is achieved using the EETF module's dual-branch structure to generate interactive enhancement features. Finally, pixel-level segmentation results are obtained through the output layer. This invention solves the problems of low efficiency and severe loss of detail in traditional methods, and insufficient segmentation accuracy and limited generalization ability of existing deep learning models, enabling automatic segmentation of retinal vessels and optic discs.

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 provides a fundus color photo-based ROP lesion detection and partition positioning method, which independently processes lesion area positioning and anatomical area positioning tasks through a double-branch collaborative detection framework, so as to avoid the problem that the position proportion of macular and optic disc is abnormal due to retinal development difference of premature infants, and improve the detection accuracy of the retina of the premature infants. It is ensured that the circular I region accurately reflects a real anatomical structure; the feature fusion module strengthens lesion area feature response and screens key feature channels to jointly improve the target recognition capability in the complex fundus image; a multi-shape joint loss function forces the network to learn a spatial topological relation between a lesion area and a circular anatomical area so as to eliminate feature redundancy and improve detection sensitivity; therefore, the problem of low accuracy of ROP lesion detection and partition positioning is solved.

The present application provides an eye state evaluation method and an electronic device. The method comprises: obtaining an optic disc image region from a first fundus image, and generating a plurality of optic cup disc ratio evaluation results based on the optic disc image region by a plurality of first models; obtaining a first evaluation result of the eye based on the optic cup disc ratio evaluation results; performing a plurality of data augmentation operations on the first fundus image to generate a plurality of second fundus images; generating a plurality of optic nerve fiber layer defect evaluation results based on the second fundus images by a plurality of second models; obtaining a second evaluation result of the eye based on the optic nerve fiber layer defect evaluation results; and obtaining an optic nerve evaluation result of the eye based on the first evaluation result and the second evaluation result. Thus, the present application can provide a reference for doctors when evaluating the eye state of patients, thereby helping doctors to give appropriate evaluation results for the eyes of patients.

This invention discloses a convolutional neural network-based optic disc segmentation method, characterized by comprising the following steps: 1) defining a TU-Net network; 2) building a U-Net network; 3) locating the optic disc based on the output annotated image; 4) preprocessing the image; 5) building an AU-Net network; 6) setting a training strategy; 7) setting a loss function; 8) training the network and updating parameters; 9) post-processing the output image; and 10) setting evaluation criteria. This method is simple to implement, highly universal, and can reduce the impact of noise in fundus images.

The invention provides an eye fundus image-based atrophy arc classification method and device, equipment and a storage medium, and the method comprises the steps: obtaining an eye fundus image of a target object, and extracting a corresponding optic disc region and an atrophy arc region in the eye fundus image; determining target feature information based on the optic disk area and the atrophy arcing area, wherein the target feature information comprises pixel information data corresponding to the candidate images in the optic disk area and the atrophy arcing area; and when the target feature information satisfies a preset classification condition, determining a classification result of the arc-shrinking region. After the eye fundus image is preprocessed, the atrophic arc area is subjected to data statistics, the pixel information data after statistics is analyzed according to a set rule, and then atrophic arc classification and recognition are achieved. Therefore, the method can achieve the technical effects of achieving the multi-class division of the atrophic arc and improving the recognition precision.

The invention relates to the technical field of coherence tomography, in particular to a household optical coherence tomography system, which comprises a data acquisition module for acquiring an optical coherence tomography image and three-dimensional tomography data of an eyeball; the data division module is used for dividing an optical coherence tomography image into grids and determining an image quality index; the center positioning module is used for positioning the center point of the optic disc based on the low-brightness connected region; the coordinate establishing module is used for screening qualified grids based on image quality indexes and extracting a retina network feature point set to establish a coordinate system; the coordinate verification module is used for projecting the three-dimensional chromatography data to a two-dimensional plane to carry out coincidence comparison so as to verify the qualification of a coordinate system; the quality evaluation module is used for determining whether the scanning quality is qualified or not based on the coordinate system verification result and the image quality index; and the optimization adaptation module is used for dynamically adjusting a preset threshold value based on a quality evaluation result so as to optimize feature point extraction. The technical problem that cross-time data cannot be accurately compared due to inconsistent scanning poses is solved.

An embodiment of the present invention provides a method and system for identifying retinal hemorrhage areas, belonging to the field of image processing technology. The retinal hemorrhage area identification method includes: collecting image information of the user's eyes; performing optic disc and blood vessel identification in the image information based on an optic disc identification model and a blood vessel identification model, respectively, to obtain optic disc and blood vessel identification results; based on the optic disc and blood vessel identification results, performing optic disc and blood vessel peeling in the image information, respectively, to obtain peeled image information; preprocessing the peeled image information, and performing hemorrhage area identification based on the preprocessed image information to obtain an identification result. The solution of the present invention performs retinal hemorrhage identification in images after optic disc and retinal peeling, ensuring identification accuracy.