4 results about "Retina eye" patented technology

The present application relates to a retinal blood vessel segmentation method based on neural ordinary differential equation, which solves the defects of poor scale adaptability and insufficient extraction of fine blood vessel features compared with the prior art. The present application comprises the following steps: obtaining a retinal fundus image and pre-processing; constructing a retinal blood vessel segmentation network; training the retinal blood vessel segmentation network; obtaining the retinal blood vessel segmentation result of the fundus image. The encoder and decoder paths are stacked by the neural ordinary differential equation module, which uses its continuous dynamic characteristics to adaptively adjust the effective receptive field by calculating the feature derivative to adapt to different scale blood vessel features; the mixed attention perception module is located at the jump connection, which adopts the "asymmetric convolution decomposition" architecture, combines channel mixing and multi-scale spatial attention, and accurately captures fine linear blood vessel features while suppressing background noise.

An AI-based retinal image analysis system for the automated detection of diabetic retinopathy, comprising the following: an image acquisition unit consisting of an optical arrangement with a coaxial illumination source, a multi-lens arrangement and an image sensor for capturing retinal images; a preprocessing unit that is operationally coupled with the image acquisition unit and is configured to receive the retinal fundus images and perform image normalization including illumination correction, noise reduction and contrast enhancement; a feature extraction unit comprising at least one processor configured to perform a variety of folding operations on the preprocessed retinal fundus images to generate hierarchical feature representations corresponding to the anatomical structures and pathological regions of the retina; a classification unit comprising at least one processor configured to process the hierarchical feature representations and produce a severity classification output corresponding to the stages of diabetic retinopathy; a storage unit that is operationally coupled to the feature extraction unit and the classification unit and stores the trained parameters assigned to the feature extraction unit and the classification unit; and a display unit configured to show the user the severity classification output and the corresponding diagnostic information.

The application discloses an eye age difference index construction method and system based on fundus images and electronic equipment, and relates to the technical field of image processing. The application trains the constructed double-channel fusion attention network model by adopting a training set and a pairing matrix loss function, can reduce the prediction error between samples in addition to the mean square error, and makes the prediction accuracy of the trained double-channel fusion attention network model further improved. The trained network model is used as a fundus age prediction model to analyze the left and right eye retina fundus images of a to-be-measured object, and the characteristics are fused, so that more accurate fundus age can be obtained, so as to accurately construct the eye age difference index, and then provide a favorable basis for judging the aging track degree of human health.

This invention discloses a retinal vessel segmentation method based on orientation field guidance and graph neural network optimization, belonging to the field of image analysis and medical image processing technology. The invention first acquires retinal fundus images and their corresponding vessel annotation maps, and preprocesses the raw retinal fundus images. Then, it constructs a retinal vessel segmentation model based on orientation field guidance and graph neural network optimization. This model includes a backbone segmentation model based on a UNet++ network architecture and a post-processing refinement module based on a visual graph neural network. The preprocessed image is input into the backbone segmentation model to obtain a preliminary vessel segmentation probability map, which is then processed by the post-processing refinement module to obtain the final vessel segmentation result. Experiments show that this invention effectively addresses the shortcomings of existing retinal vessel segmentation methods in terms of small vessel extraction, structural connectivity preservation, and background noise suppression.