10 results about "Optic disc segmentation" patented technology

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 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.

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 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 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 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 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.