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

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.

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.

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.

A multivariable artificial intelligence-based monitoring system for early detection of glaucoma and method thereof. The multivariable artificial intelligence system comprises a computing device having a control unit and one or more non-transitory storage devices for storing instructions to be executed by the control unit. The computing device is in communication with an application server via a network. The computing device includes an input module, an image enhancing module, a feature extraction module, a post image processing module, and a parameter selection module. The proposed multivariable artificial intelligence system provides a deep learning architecture to segment the optic disc and optic cup in two ways using two different networks termed multi spatial attention feature fusion network (MSAFF-Net) and multi dilated edge extraction network (MDEE-Net) respectively.

The invention provides a multi-modal retinal disease intelligent auxiliary diagnosis system, which comprises an image preprocessing module used for processing a binocular fundus image uploaded by a user; the disease classification module is used for extracting global features and local lesion details by adopting a dual-channel DINOv2 model, initializing weights in a data set through transfer learning, performing fine adjustment on the fundus image data set, and performing fundus multi-label classification; and the batch processing module is used for disassembling batch binocular eye fundus image processing tasks into independent sub-tasks based on a distributed task scheduling and dynamic resource allocation technology, and distributing the independent sub-tasks to a plurality of edge computing nodes for parallel processing. According to the method, a multi-label independent classifier is designed, eight independent three-layer MLP classifiers are adopted, and each classifier focuses on single pathological feature modeling. Through parameter space decoupling design, gradient conflicts among multiple labels are avoided, and accurate capture of heterogeneity pathologies such as diabetes microvascular leakage characteristics and glaucoma optic cup morphological parameters is ensured.

The application discloses a kind of based on double-path self-supervision's cross-domain view cup view disc automatic segmentation method and device, method includes: using source domain-target domain and target domain-source domain's two-way fundus image domain transformation, respectively generate target domain style source domain image and source domain style target domain image, the source domain image has segmentation label, target domain image is no segmentation label;Using DeepLabV3+ image segmentation network as framework, MobileNetV2 feature extractor is matched, constructs double-path fundus image segmentation network;The prediction segmentation result of the unsupervised image output by double-path network is fused, and the fusion pseudo label is obtained, and the further training of no label image is guided using fusion pseudo label;Using a cross-domain contrast constraint mechanism, the similarity of image features before and after domain transformation is optimized, so that the information related to image structure is retained in features.The device comprises a memory and a processor.The application improves the accuracy of target domain image prediction segmentation result.