33 results about "Retina vessels" patented technology

The invention discloses a method to prevent and treat retina injury of mammal, which comprises the following steps: giving effective dispense erythrocyte-stimulating factor (EPO) into vitreous chamber of injured eye; finishing the method. The EPO possesses the function of protecting retina vessel and neuron, which does not cause imperfect effect for other organ or tissue with local give medicine.

Retinal Vascular Image Segmentation System Based on Global Information Convolutional Neural Network. The invention relates to a retinal vessel image segmentation system. The invention aims to solve the problems of limited utilization of global information and easy loss of important features in the existing convolutional neural network retinal vessel image segmentation. The system of the present invention includes: an image processing main module, a neural network main module, a training main module and a detection main module; the image processing main module is used to collect original retinal images, preprocess the collected original retinal images, and process The final image is input into the training main module and the detection main module; the neural network main module is used to establish a convolutional neural network capable of extracting global information and strengthening features; the training main module is used to initialize network parameters and obtain a trained volume A product neural network model; the detection main module is used to use the trained model to test and calculate the model performance index. The invention belongs to the field of retinal blood vessel image segmentation system.

The invention relates to a retina blood vessel image segmentation method based on improved UNet++, and belongs to the technical field of medical image processing. The invention selects the deep supervision network UNet++ as the image segmentation network model to improve the use efficiency of features; introduces the MulitRes feature extraction module to improve the feature learning effect of small blood vessels in a low-contrast environment. The generalization ability of the network and the expressive ability of the network structure are further improved, and the SeNet channel attention module is added after feature extraction to perform squeezing and excitation operations, thereby enhancing the receptive field in the feature extraction stage and increasing the weight of target-related feature channels. Based on the DRIVE retinal image data set, the improved UNet++ network model of the present invention is verified, and the evaluation indicators such as overlap ratio, intersection ratio, accuracy, and sensitivity have been improved to a certain extent compared with the existing model.

The invention relates to a method and device for measuring blood oxygen saturation of a fundus retina. The method is characterized by comprising the following steps of: taking a platform based on an ASOLO (adaptive optics confocal scanning laser ophthalmoscope), selecting a light with at least two different wavelengths as the light source of the ASOLO, enabling the retina to be sequentially imaged after correcting a fundus aberration by adopting the adaptive optics; generating defocus via a distorting lens for realizing the longitudinal chromatography of the retina so as to make the same position of a retina vessel imaged; registering the obtained high-resolution retinal image with multiple wavelengths, extracting a plurality of darkest spots and the spot arranged at a fixed distance fromthe darkest spots and in a tissue along with the vessel; and processing data to obtain the blood oxygen saturation of the vessel. In the invention, the high-resolution retinal image can be obtained by adopting the adaptive optics to correct the fundus aberration, and the blood oxygen saturation of the arteries, veins and capillaries of the fundus retina can be measured by processing a multi-wavelength image.

The present invention proposes an automatic retinal vessel segmentation method for clinical diagnosis of glaucoma. By fusing five models of matching filter, neural network, multi-scale line detection, scale-space analysis and morphology that depend on different image processing techniques, the optic disc is eliminated. and the effects of bright areas such as exudates. At the same time, the present invention does not require a large amount of data to establish a retinal vessel segmentation model, which greatly reduces the amount and complexity of data to be processed, is easy to implement, and can effectively improve the efficiency of retinal vessel segmentation. The present invention also uses the region growing method and gradient information to iteratively grow the background and blood vessel regions on the basis of the multimodal fusion results. The segmentation results have better continuity and smoothness, and can retain more details of retinal blood vessels and more Complete visual retinal vascular network, thereby effectively assisting ophthalmologists in diagnosing diseases and reducing the burden on ophthalmologists.

The invention relates to the application of deep learning algorithms in the field of medical image analysis, in particular to a retinal vessel segmentation algorithm that integrates W-net and conditional generation confrontation network. The present invention better solves the problems of low segmentation sensitivity and insufficient segmentation of microvessels, and achieves great progress in network parameter utilization, information flow and feature resolution, and contributes to complete segmentation of main vessels and fine segmentation of microvessels , and the intersection of blood vessels is not easy to break, and the lesion and optic disc are not easy to be mistakenly divided into blood vessels. The present invention integrates multiple network models with relatively low complexity, and has excellent overall segmentation performance on the DRIVE data set, with sensitivity and accuracy rates of 87.18% and 96.95% respectively, and a ROC curve value of 98.42%, which can be used in medical treatment Computer-aided diagnosis in the field, enabling rapid and automated retinal vessel segmentation.