6 results about "Retinal vessel" patented technology

Disclosed are methods and devices useful for imaging blood-containing tissue, for example for angiography, especially retinal angiography, whereby an image is generated by dividing pixels of an image acquired at some wavelength range by corresponding pixels of an image acquired at a different wavelength range. In some embodiments, a first wavelength range includes predominantly light having wavelengths between about 400 nm and about 620 nm and a second wavelength range includes predominantly light having wavelengths between about 620 nm and about 800 nm.

The invention discloses a lightweight OCTA retinal vessel segmentation method based on a full-convolution full-resolution network. According to the method, an encoder and two decoders are adopted; the encoder is combined with the thought of cyclic convolution and a ConvNeXt convolution module of a full-resolution design to extract a full-resolution feature map under the condition of not carrying out down-sampling so as to reserve information of small blood vessels to the maximum extent; dynamic snakelike convolution is introduced into the main decoder for self-adaptive fitting of the slender tubular shape of the blood vessel; the secondary decoder is mainly composed of a depth separable convolution and a point convolution so as to provide an additional supervision signal for the encoder in a training stage; according to the method, good balance between the segmentation performance and the calculation efficiency can be realized, the segmentation performance equivalent to the segmentation performance in the prior art can be realized only by using lightweight calculation cost, and the method has application potential in a computing resource limited scene.

The application discloses an OCTA image retinal blood vessel segmentation method based on deep learning, and comprises the following steps: building a double-branch axial compression convolutional neural network segmentation model; training and optimizing parameters of the built model; using the trained model, quickly positioning and accurately segmenting the retinal blood vessel structure based on OCTA to obtain a retinal blood vessel segmentation result binary graph. The OCTA image retinal blood vessel segmentation method based on deep learning has the characteristics of high accuracy and fast processing speed.

This invention discloses a method, apparatus, storage medium, and device for retinal vessel image segmentation, belonging to the field of image segmentation technology. The method includes acquiring retinal vessel images and inputting them into a trained retinal vessel segmentation model. Accurate segmentation of retinal vessels is achieved through newly introduced topological feature optimization and local and global feature optimization. This invention improves the modeling effect of vessel topology by introducing topological feature optimization into the encoder, and achieves accurate extraction of vessel details from different scales to the global level by introducing local and global feature optimization into the encoder, reducing the risk of vessel discontinuities and missed detections in the segmentation results.

The invention discloses a multi-resolution retinal blood vessel image segmentation method based on a dynamic strategy. A used model comprises an encoder and a decoder, the encoder comprises a plurality of encoding layers, the decoder comprises a plurality of decoding layers, and an edge perception multi-scale enhancer and a dynamic multi-scale feature fusion module are embedded in jump connection between each encoding layer and the corresponding decoding layer; in the edge perception multi-scale enhancer, the input features are subjected to 3 * 3 convolution to extract local features; the local features are subjected to 1 * 1 convolution to extract initial features, the initial features are sequentially subjected to three times of convolution and average pooling to obtain multi-scale features, the multi-scale features are subjected to edge feature information enhancement through an edge enhancement module, and after the multi-scale features subjected to edge enhancement and the initial features are spliced, convolution is carried out to obtain multi-scale feature information; and obtaining the output characteristics of the edge perception multi-scale enhancer. The blood vessel feature information with large morphological difference is extracted and fused, so that the model learns more effective blood vessel feature information, and the segmentation precision is improved.

This item is an image that displays information using a GUI. This GUI is an image that has the function of displaying the results of eye examinations in ophthalmology. Specifically, the image is divided into upper and lower sections, and the upper and lower sections display the results of the examination, such as a fundus photograph, a circular map showing the thickness of the retina, a cross-sectional image of the retina, and the reticular structure of the retina's blood vessels, respectively, and the image has the function of displaying the examination results in a way that allows comparison between the upper and lower sections. The roughly circular area on the left side of the image displays information related to the fundus photograph. The roughly square area on the left side of the center of the image displays information related to the circular map showing the thickness of the retina. Furthermore, the horizontal rectangle on the right side of the center of the image displays information related to the cross-sectional image of the retina. The roughly square area on the right side of the image displays information related to the reticular structure of the retina's blood vessels. When in use, it is displayed as shown in "Reference Figure 1 showing the usage state," and when it is a monochrome image in use, it becomes "Reference Figure 2 showing the usage state."