24 results about "Optic disc segmentation" patented technology

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 provides a glaucoma optic cup and optic disc segmentation method based on contour injection content. The method comprises the following steps: inputting a fundus retina color image into a coding module for coding; using a contour decoder to carry out visual cup and optic disk contour decoding on the multi-level features stage by stage; performing content segmentation decoding of the optic cup and the optic disk on the multi-level features by using a content decoder; a contour injection module is used for receiving the contour decoding features of the corresponding optic cup and optic disk in different stages to conduct contour injection on the content decoding features of the optic cup and optic disk in the first stage, and content decoding of a content decoder in the next stage is guided in an iteration mode to obtain optic cup and optic disk content segmentation mask graphs. According to the invention, the encoder and the decoder based on the large-size convolution kernel are different from a design normal form that a traditional convolution network takes a 3 * 3 window as a main part, the receptive field is effectively expanded, and a more global feature can be obtained by using a calculation amount smaller than global self-attention.

The invention discloses an optic cup and optic disc segmentation method and system based on improved U-Net, and particularly relates to the technical field of medical image processing. The method includes: performing standardized preprocessing on an eye fundus image; the U-Net model is optimized and improved, and an improved model LFM-Net is obtained; the improved model LFM-Net comprises a multi-scale feature enhancement aggregation module and a trans-attention feature fusion module; the multi-scale feature enhancement aggregation module is used for capturing feature information of different scales, and the trans-attention feature fusion module is used for realizing fusion of features of an encoder and a decoder; and inputting the preprocessed eye fundus image data set into the improved model LFM-Net for training, and realizing automatic segmentation of an optic cup and an optic disk in the eye fundus image by using the trained model. According to the invention, by enhancing multi-scale feature expression and optimizing feature fusion efficiency, the problem of insufficient segmentation precision under complex conditions such as blurred optic cup and optic disc boundaries and irregular shapes is effectively solved.

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 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.

The present invention discloses a method and device for retinal optic disc segmentation by cyclic cropping and multi-model fusion. The method includes: collecting an optic disc segmentation data set; selecting a deep learning neural network suitable for optic disc segmentation; cyclically cropping and then segmenting the optic disc; comprehensively analyzing the advantages of each neural network model, and performing model fusion. The present invention uses a variety of different neural network models and a variety of model fusion schemes, trains on multiple optic disc segmentation data sets, and evaluates the segmentation effects of each model and model fusion. The experimental results show that the model fusion scheme achieves better segmentation effects compared with any single sub-network model.

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 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.

This invention presents a passive domain adaptive fundus segmentation method based on evidence representation and marginal screening. Under the condition that the source domain training images and their pixel-level annotations are inaccessible, a Student network and a Teacher network are constructed to learn segmentation of the target domain fundus image. The network outputs positive foreground evidence and negative background evidence for each segmentation channel, and constructs a pixel-level Beta distribution. Based on the Beta distribution, foreground probability, evidence margin, and evidence strength are calculated. Reliable positive pseudo-labels are obtained through joint screening, and a weighted segmentation loss is constructed by combining pseudo-label weights. Simultaneously, regularization loss and evidence strength constraint loss are introduced to optimize the Student network. Finally, the exponential moving average coefficient is dynamically adjusted based on the amount of evidence to update the Teacher network parameters. This method can effectively suppress the propagation of pseudo-label noise and improve the accuracy, stability, and cross-domain adaptability of optic cup and optic disc segmentation in the target domain fundus image.

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.

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 discloses a fundus image optic cup and optic disc segmentation method and device based on edge enhancement and visual features, and belongs to the technical field of image segmentation, and the method comprises the steps: employing an optic disc ROI coarse positioning model to carry out the positioning of an obtained to-be-segmented fundus image, and generating an optic disc ROI image; a Laplace feature map and a semantic vector are extracted from an optic disk ROI image by using an optic cup and optic disk segmentation model, multilayer feature coding is performed on the semantic vector to obtain a coding feature map, edge feature enhancement is performed on the coding feature map of each level according to the Laplace feature map and the decoding feature map of the previous level, and an edge enhancement feature map of each level is generated. And decoding each edge enhanced feature map, a fusion feature map of the coding feature map of the same level and the decoding feature map of the previous level to obtain a decoded feature map, and finally performing pixel classification on the decoded feature map to obtain an optic cup and optic disk segmentation result. According to the method, the segmentation precision of the optic cup and the optic disc is improved while the relatively high calculation efficiency is maintained.

The present invention proposes a method for glaucoma optic cup and optic disc segmentation with contour injection content, the method comprising: inputting a fundus retinal color image into a coding module for encoding; using a contour decoder to decode the contours of the optic cup and optic disc in stages for multi-level features; using a content decoder to decode the content segmentation of the optic cup and optic disc for multi-level features; using a contour injection module to accept contour decoding features corresponding to different stages of the optic cup and optic disc to perform contour injection on the content decoding features of the first stage of the optic cup and optic disc, and iteratively guide the content decoding of the next stage content decoder to obtain a optic cup and optic disc content segmentation mask map. The present invention is based on an encoder and decoder with a large-size convolution kernel, which is different from the design paradigm of a traditional convolutional network based on a 3×3 window, effectively expanding the receptive field, and can obtain more global features with a computational amount less than that of global self-attention.

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 application discloses a kind of dense lesion semi-automatic labeling method for fundus image, method includes constructing and training dense lesion segmentation network, macula fovea positioning network and optic cup optic disc segmentation positioning network;Utilize network to process to obtain dense lesion prediction contour area, macular area and optic cup optic disc area respectively;Three labelers are assigned to fundus image, each labeler selects key area and other area from macular area and optic cup optic disc area and carries out labeling operation, to obtain first round labeling result;According to first round labeling result, the lesion labeling consistency index between every two labelers is calculated to determine two labelers to execute second round lesion labeling, and second round labeling result is obtained;Second round labeling result is audited to obtain final labeling result.Therefore, the problem of large difficulty in dense lesion labeling and large difference in labeling by different personnel is solved, and the labeling efficiency is improved, the labeling time is reduced, and the labeling cost is reduced.

The invention relates to the field of eye fundus color photo optic cup and optic disc segmentation, and provides an eye fundus color photo optic cup and optic disc segmentation method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a to-be-segmented eye fundus color photo; inputting the fundus photo to be segmented into the trained fundus photo optic cup and optic disc segmentation model to obtain a corresponding optic cup and optic disc segmentation result; the fundus color photo optic cup and optic disc segmentation model is constructed by replacing an encoder in a DPT architecture with a DINOv2 encoder. The training process comprises a process of diffusion training of a DINOv2 encoder, a consistency training process combining image-level spatial transformation and feature-level hidden space disturbance, and a process of adaptive optimization of pseudo labels in the consistency training process based on category prototype guidance. According to the method, the utilization rate of limited annotation data is improved, the medical image annotation cost is remarkably reduced, meanwhile, the image segmentation precision is ensured, and reliable technical support can be provided for eye disease diagnosis.

The present application belongs to the technical field of optic disc segmentation, and an embodiment thereof provides an optic disc segmentation unsupervised evaluation method and device based on morphology and learning unification. The method comprises: comparing the feature representation of the vector matrix formed by the topological morphology parameter vector of the to-be-evaluated optic disc image and the topological morphology parameter vector of the reference image set, obtaining a first evaluation result according to the similarity; inputting the feature vector of the to-be-evaluated optic disc image into a trained single classification learning model based on an autoencoder, and obtaining a second evaluation result according to the similarity between the input and the output of the single classification learning model; and obtaining a comprehensive evaluation result of the to-be-evaluated optic disc image by comprehensively combining the first evaluation result and the second evaluation result. The embodiment of the present application can automatically obtain an objective and quantitative evaluation result, and is more accurate and reliable.

The present invention relates to artificial intelligence and digital medical technology, and an embodiment thereof provides a method, device, computer, and storage medium for optic cup and optic disc segmentation. The method comprises the following steps: acquiring an original fundus image; performing feature region extraction on the original fundus image to obtain a candidate region image; inputting the candidate region image into an optic disc detection network branch, a cup and disc segmentation network branch, and a quality scoring network branch for training to obtain a target neural network module, wherein the optic disc detection network branch is used to determine optic disc position information in the candidate region image, the cup and disc segmentation network branch is used to segment the candidate region image and output a cup and disc segmentation image, and the quality scoring network branch is used to perform quality scoring on the cup and disc segmentation image; and inputting the original fundus image into the target neural network module to obtain a target cup and disc segmentation image output by the target neural network module, thereby optimizing the segmentation process and effectively improving the accuracy of segmentation.

The invention discloses a lightweight optic disc segmentation method and system, and relates to the technical field of medical image segmentation. Comprising the following steps: carrying out data enhancement processing on a public data set iChallenge-PM, an iChallenge-AMD and a Drishiti-GS; a lightweight segmentation model is constructed, and the model comprises a C2f module used for extracting and fusing eye fundus image features; a GlobalEdgeInformationTransfer module, which is used for enhancing the edge feature extraction and the multi-scale fusion of the eye fundus image; the Wavelet Pooling module is used for realizing eye fundus image resolution conversion by using a fixed wavelet filter and reducing trainable parameters; and the C2fgConv module introduces a gating mechanism to optimize eye fundus image feature extraction and reduce parameter quantity. Through multi-scale feature extraction and fusion, WaveletPooling application and C2fgConv design, while the optic disk segmentation precision is ensured, model parameters are effectively reduced, light weight is realized, the training efficiency and generalization ability are improved, and the method is suitable for optic disk segmentation tasks under different resource conditions.

The invention discloses a focus segmentation method for a diabetic retinopathy fundus image. The focus segmentation method comprises the following steps: S1, collecting wide-angle fundus image data of diabetic retinopathy; and S2, based on a multi-scale feature extraction strategy, capturing a global structure and local details in the wide-angle fundus image, identifying typical features of common disease species of diabetic retina, judging whether the common disease species of fundus exist or not, if so, outputting a corresponding disease species prediction report, and if not, performing diabetic retinopathy staging on the wide-angle fundus image to obtain a diabetic retinopathy staging result. Outputting the staging category with the highest probability; s3, constructing and training a focus segmentation model and an optic cup and optic disc segmentation model; s4, segmenting the focus of the wide-angle fundus image through the focus segmentation model and the optic cup and optic disc segmentation model, and performing pixel-level labeling on the optic cup and optic disc of the focus; and S5, generating a corresponding screening evaluation report according to the staging category in the step S2 and the focus segmentation result in the step S4.

The invention discloses a fundus image optic cup and optic disc segmentation method based on improved U-net, and belongs to the technical field of medical image processing. Comprises: acquiring a fundus image data set; an improved U-net network is constructed; a CrossFormer layer is added between the last layer of the encoder and a decoding structure; an ASPP detection head is embedded at the last end of the decoder; the method comprises the following steps of: constructing a compound type loss function in which Dice Loss and Cross Entry Loss are combined; training an improved U-net network by using the fundus image data set; and inputting a fundus image to be segmented into the trained improved U-net network for segmentation to obtain an optic cup and optic disc segmentation result. Based on the improved U-net, the problems that blood vessels penetrate through the optic cup and optic disc area, the edge is fuzzy, the proportion is small, and accurate segmentation is difficult are solved.

The invention discloses a fundus image lesion segmentation and quantitative analysis method based on data enhancement, and belongs to the technical field of fundus, and the method comprises the following steps: S1, image preprocessing; s2, optic disc and macular positioning; s3, optic disc segmentation; s4, myopia bottom feature change recognition; and S5, leopard spot region feature quantification. The problems that accurate segmentation and quantitative analysis cannot be carried out on the focus of the fundus image, a high-quality diagnosis result and an accurate treatment scheme cannot be provided, the diagnosis accuracy is reduced, and the fundus treatment effect of a user is poor in the prior art are solved. The advanced artificial intelligence technology is utilized to deeply analyze and process the fundus image, myopia-related fundus changes such as leopard spot, diffuse chorioretinal atrophy and plaque-like chorioretinal atrophy can be accurately identified and quantitatively evaluated, and a visual heat map and a quantitative report are generated, so that myopia-related fundus changes can be accurately identified and quantitatively evaluated. A comprehensive and objective diagnosis basis is provided for doctors, and the doctors are assisted to formulate a treatment scheme more accurately.

The invention provides an optic disc segmentation method. The optic disc segmentation method comprises the following steps: acquiring an optic disc image; an encoder is used for extracting multi-scale features of the optic disk image, the encoder comprises a plurality of variable waveform convolution blocks FWC Block, and each variable waveform convolution block FWC Block dynamically adjusts a receptive field through a dynamic convolution layer AKConv and performs feature compression in combination with a wavelet down-sampling layer HWD; a decoder is used for gradually recovering the resolution of the feature map, the decoder comprises a plurality of selective double attention fusion gates SDA Gate, and the features of the encoder and the decoder are fused through a channel attention and space attention mechanism to obtain a fused feature map; and obtaining an optic disc image segmentation result according to the fused feature map.