45 results about "Optic cup/disc ratio" patented technology

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.

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.

A non-stereo fundus image is used to obtain a plurality of glaucoma indicators. Additionally, genome data for the subject is used to obtain genetic marker data relating to one or more genes and/or SNPs associated with glaucoma. The glaucoma indicators indicators and genetic marker data are input into an adaptive model operative to generate an output indicative of a risk of glaucoma in the subject. In combination, the genetic indicators and genome data are more informative about the risk of glaucoma than either of the two in isolation. The adaptive model may be a two-stage model, having a first stage in which individual genetic indicators are combined with respective portions of the genome data by first adaptive model modules to form respective first outputs, and a second stage in which the first outputs are combined by a second adaptive mode. Texture analysis is performed on the fundus images to classify them based on their quality, and only images which are determined to meet a quality criterion are subjected to an analysis to determine if they exhibit glaucoma indicators. Also, the images are put into a standard format. The system may include estimating the position of the optic cup by combining results from multiple optic cup segmentation techniques. The system may include estimating the position of the optic disc by applying edge detection to the funds image, excluding edge points that are unlikely to be optic disc boundary points, and estimating the position of an optic disc by fitting an ellipse to the remaining edge points.

A glaucoma diagnosis apparatus according to an embodiment includes a fundus image processor configured to receive a fundus image and extract a first region of interest (ROI) and a second ROI from the received fundus image, an image classification neural network configured to learn the extracted first ROI and perform classification into a normal fundus image and a glaucoma fundus image on the basis of the learned first ROI, a vertical cup-to-disc ratio (vCDR) calculator configured to recognize an optic disc (OD) and an optic cup (OC) from the extracted second ROI and calculate a vCDR, and a determinator configured to aggregate a vCDR calculation result and an image classification result of the image classification neural network to determine whether glaucoma is present in the fundus image.

The invention relates to the field of histopathology, and particularly discloses a preparation method of an animal eyeball pathological section. The preparation method comprises the steps of: (1) eyeball extraction; (2) eyeball fixation, which is implemented by putting an separated eyeball into a stationary liquid which is pre-cooled to 4.0 DEG C, and putting the stationary liquid containing the eyeball into a refrigerator of which the temperature is set to 4.0 DEG C for fixation for 48 hours, wherein the stationary liquid comprises the following components: 4% paraformaldehyde solution, anhydrous acetic acid and absolute ethyl alcohol in a volume ratio of 10:(1.5-2.0):(8.0-8.5); (3) dehydration and transparent processing of an eyeball optic cup; (4) wax dipping and embedding; (5) and slicing. The preparation method is beneficial to preparation of the high-quality eyeball pathological section so as to facilitate improvement of basic application of retina related research.

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.

The embodiment of the invention provides a fundus image classification system based on integrated deep learning. The fundus image classification system comprises a pre-diagnosis classification network, a segmentation network and a final diagnosis module. The pre-diagnosis classification network obtains an initial diagnosis result based on the global information of a target fundus image; the segmentation network performs image segmentation on the optic disc, optic cup and optic nerve fiber layer states of the target fundus image based on the initial diagnosis result; and the final diagnosis module extracts a vertical cup-to-disk ratio and an ISNT score based on a result of optic disk, optic cup and optic nerve fiber layer state segmentation, and acquires and displays a final category of thetarget eye fundus image based on the vertical cup-to-disk ratio, the ISNT score and an optic nerve fiber layer defect state. According to the embodiment of the invention, firstly, glaucoma pre-diagnosis is carried out on a target fundus image, and a proper target segmentation network is selected, so that the segmentation precision is improved; and when glaucoma judgment is carried out, the accuracy of a classification result is further improved by combining a plurality of quantitative indexes capable of reflecting the disc edge form.

The invention discloses a joint segmentation method for optic cups and optic disks in fundus medical images, which comprises the following steps: firstly, constructing a joint segmentation network based on a UNet network, and introducing a global information extraction module and a multi-path hole convolution module into the joint segmentation network; and inputting a fundus medical image to be processed into the joint segmentation network for joint segmentation of the optic cups and the optic disks. According to the method, global context information and multi-scale context information in thefundus medical image can be fully extracted, and the joint segmentation effect of optic cups and optic disks in the fundus medical image is improved.

The invention discloses a training device for glaucoma recognition. The training device comprises an acquisition module; the image segmentation network is an artificial neural network based on deep learning and is trained through the preprocessed fundus image, the annotated image and the spatial weighted graph so as to output the probability that each pixel point in the preprocessed fundus image belongs to an optic disk and the probability that each pixel point belongs to an optic cup; generating an optic disc area image and an optic cup area image based on the probability that each pixel point in the preprocessed fundus image belongs to the optic disc and the probability that each pixel point belongs to the optic cup; the feature extraction module is used for acquiring glaucoma features based on the optic disc region image and the optic cup region image; and a classifier trained by feature information including glaucoma features and a glaucoma classification tag based on machine learning to output a probability belonging to glaucoma. According to the scheme, the accuracy of glaucoma recognition can be improved.

The invention discloses a multi-mechanism adaptive eye fundus image segmentation method, which belongs to the field of artificial intelligence medical image processing, constructs a multi-mechanism adaptive Faster R-CNN eye fundus image segmentation network, and adopts a network global loss function to guide optic disk and optic cup segmentation. The method specifically comprises the following steps: collecting an eye fundus image data set, and performing preprocessing; the preprocessed data set is input into the constructed multi-mechanism adaptive Faster R-CNN eye fundus image segmentation network; a network global loss function is adopted to guide accurate segmentation of an optic disk and an optic cup; eye fundus images are collected in real time and input into a multi-mechanism adaptive Faster R-CNN eye fundus image segmentation network, an optic disc and an optic cup are accurately segmented by using a global loss function in the network, and clinical diagnosis of doctors is assisted. According to the method, accurate segmentation of the optic disc and the optic cup in the eye fundus image is realized, and the method has relatively good generalization and robustness.

According to the fundus photo classification method and the fundus image processing method and system, the technical problems that an existing fundus photo classification method is low in efficiency and large in error can be solved. Comprising the steps of obtaining and preprocessing a fundus photo to generate a standardized fundus picture; dividing the processed fundus photos into a training set and a test set, and performing data amplification on the training set; performing data preprocessing on the training set and the test set, and ensuring that each training data set (batch) input into the model in the training stage is uniform in category; integrating learning strategies, and training a branch neural network model; and carrying out model fusion to obtain a final detection model so as to realize classification of the fundus photos. The device is high in effect and excellent in automation degree; according to the method, features such as the optic cup/optic disk ratio and the arteriovenous ratio of the fundus photo are extracted, classification is carried out in combination with a machine learning method, the calculation speed is high, and few computer resources are occupied during operation.

The invention relates to the field of medical image processing, and provides a fundus image segmentation method and system of a full convolutional neural network based on an Attention mechanism, and acomputer readable storage medium, the method comprising: selecting fundus retina image data as a training set and a test set; preprocessing the fundus retina images in the training set; constructinga full convolutional neural network model on the TensorFlow; and segmenting the test set by using the trained full convolutional neural network model to obtain a final segmentation result. The systemcomprises a data acquisition module, a preprocessing module, a full convolutional neural network construction module and an image segmentation module. According to the multi-connection complete convolutional neural network model based on the Attention mechanism, the optic disc of the optic cup is automatically segmented from the fundus image, various limitations of a traditional method are overcome, the learning ability of the model is improved by fusing multi-level features in the neural network, and the accuracy of cup disc segmentation is improved.

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.

The invention provides an image evaluation method and device, a computer readable storage medium and electronic equipment, and relates to the technical field of image processing. The image evaluation method comprises the following steps: determining optic cup and optic disc feature information corresponding to a fundus image to be evaluated; determining nerve fiber injury information corresponding to the fundus image to be evaluated; and determining glaucoma assessment information corresponding to the fundus image to be assessed based on the optic cup and optic disc feature information and the nerve fiber injury information. Intelligent evaluation is carried out on the early risk of glaucoma, the severity and the change degree of glaucoma and the like according to the optic cup and optic disc feature information in combination with the nerve fiber injury area, so that doctors are helped to carry out glaucoma diagnosis more accurately.

The invention discloses a fundus retina image segmentation method based on a deep convolutional neural network. The method employs the deep convolutional neural network to map features of a vascular tissue, an optic disc optic cup tissue and a lesion tissue in a medical image, and employs the convolutional network to segment the image. In addition, in order to increase segmentation accuracy, a new data preprocessing method of the fundus retina image is used for enhancing the image; an end-to-end deep convolutional network is used for solving a problem of small blood vessel segmentation, and deep significant features of a lesion area are obtained and visualized; a series of problems caused by large pixels of various medical images are solved by using a method of combining multiple deep neural networks.

A two-way cross connection convolutional neural network for image segmentation performs accurate segmentation while executing different interest targets in a multi-modal medical image, and achieves effective extraction of different interest targets by introducing two different network branches and a new cross jump connection in an existing BiO-Net segmentation network. The segmentation experiment based on the disclosed eye fundus image shows that the optic disc and optic cup areas in the eye fundus image can be effectively extracted, and the segmentation performance superior to that of existing networks such as U-Net and BiO-Net is obtained.

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.