13 results about "Retina vessels" patented technology

This invention provides a method and apparatus for cross-domain segmentation of medical images based on an adaptive frequency domain alignment network. The method includes: acquiring a source domain medical image set and a target domain medical image set; preprocessing the source domain and target domain medical image sets; training an AFDAN model based on the preprocessed source domain and target domain medical image sets; optimizing the trained AFDAN model to obtain a final AFDAN model; and performing segmentation inference on the target domain image based on the final AFDAN model to obtain a high-precision medical image segmentation result. The system, method, and apparatus provided by this invention are suitable for high-precision segmentation of medical images such as vitiligo lesions and retinal vessels in scenarios where labeled data is scarce.

The invention provides a multi-branch enhanced retinal vessel segmentation network method based on stimulation guidance, and belongs to the technical field of medical image intelligent diagnosis. The technical problems that small blood vessels are difficult to accurately identify and boundaries are not clear in retinal blood vessel segmentation are solved. According to the technical scheme, the method comprises the following steps: S1, collecting fundus color image data to be segmented; s2, constructing a boundary enhancement module; s3, constructing a multi-scale feature aggregation module; s4, constructing a stimulation guide gating fusion module; and S5, after model training is completed, for each to-be-segmented test image. According to the method, boundary gradient enhancement, multi-scale interaction and stimulation guide gating cooperate to improve boundary continuity and detail fidelity, thin blood vessel distinguishability is improved, and robustness under a complex background and weak contrast is enhanced.

The invention discloses a retinal vessel segmentation method of a multi-scale residual U-Net model based on multi-tense features, and belongs to the field of computer vision and medical image processing. According to the method, double residual convolution (DResConv) is adopted to remarkably enhance the context feature extraction capability and improve the segmentation performance of small blood vessels under a complex background. In addition, a mixed downsampling block (MDB) combining an attention mechanism (SKNet) and a pooling operation is adopted, so that the detail loss is effectively reduced, and abundant blood vessel information is reserved at the same time. And finally, channel and space attention are introduced through a multi-scale multi-time fusion module (MFM), a richer multi-scale feature map is generated, and multi-scale feature expression at different moments is effectively realized. The method is more excellent in color fundus retina blood vessel segmentation tasks.

The application belongs to the field of deep learning image segmentation, and discloses an OCTA image microvessel segmentation method fusing multi-view features, which comprises the following steps: splitting input 3D data and 2D image data as training set, verification set and test set respectively; meanwhile, obtaining the blood vessel label map of the 2D image; pre-processing and data enhancement are performed on the input data; the pre-processed 3D data is input into a 3D feature extraction network to extract multi-scale spatial features of the 3D image; the pre-processed 2D data is input into a 2D feature extraction network to extract multi-scale features of the 2D image; the 3D features and the 2D features are input into a cross-dimension feature fusion network to generate fusion features; the fusion features are input into a decoder to generate a prediction map of retinal blood vessels; the application can provide a more objective and accurate evaluation method for the diagnosis and research of diseases related to retinal blood vessels, and can quickly and accurately segment out retinal microvessels.

The invention relates to the technical field of image matching methods, provides a medical image matching method and device, and solves the problem of insufficient focus area matching accuracy in the prior art. The method comprises the following steps: aligning two fundus images at different time points based on key points of a retinal vascular network; performing multi-scale decomposition on the aligned image to obtain a multi-scale frequency sub-band; forming a propagation path through local extreme points of signal intensity in a cross-scale link sub-band, and respectively determining candidate focus seed points in the two images; taking the seed point as a starting point, performing region growth according to the signal intensity gradient direction of the frequency sub-band, and defining a focus contour; and mapping the lesion contours to a retinal vessel topological space, analyzing and comparing the consistency of the structure and space relationship of topological sub-graphs corresponding to the lesion contours in the two images, and establishing a matching link between lesion areas. According to the invention, accurate matching and association of focus areas in fundus images collected at different time points can be realized.

The invention provides a diabetes retina vessel segmentation method based on deep learning, and the method comprises the steps: carrying out the processing of a diabetic retinopathy fundus image through a trained optimal UFNet model, and segmenting a fundus vessel image; the UFNet model inherits a Unit + + decoder thought, an SE-CA module is added in each layer of encoder of the Unit + + model, and then the feature is output after passing through a feature fusion module; wherein the SE-CA module is composed of an SE attention module and a CA attention module which are arranged in sequence; the CA attention module generates channel attention features by using two modes of global average pooling and maximum pooling, then performs convolution processing on the two pooling results, and finally obtains a channel weight through a sigmoid function; according to the method, blood vessel segmentation and labeling can be carried out on the diabetic retinopathy image at a higher speed, meanwhile, the accuracy rate is high, the requirement for real-time detection can be met, and the method is suitable for a low-computing-power platform.

The application discloses a retinal blood vessel segmentation method and system based on an automatic coding-decoding network of a discrete binary particle swarm optimization, and the segmentation method comprises the following steps: constructing a retinal blood vessel dataset; acquiring a lightweight U-shaped neural network model; acquiring an optimal U-shaped neural network model; and acquiring a segmentation result of a retinal blood vessel image through the optimal U-shaped neural network model. The discrete binary particle swarm optimization algorithm is used to automatically search and optimize the neural network structure, so that the network architecture can be automatically adjusted according to the task requirements, and meanwhile, the FPN Attention Block is introduced, the attention mechanism of the ECA-Net and the CBAM is combined, the attention mechanism is flexibly configured according to a selection factor, the encoder output is weighted, and then the decoder is fused, important local features can be effectively focused, and the segmentation precision of small blood vessels and complex structures is improved.

Described herein is a fundus image analysis system including a pre-segmentation image quality assessment module for receiving a fundus input image, and performing overall retinal image quality assessment and measurement quality assessment on the fundus input image; segmentation module for segmenting retinal vessel, artery, vein and optic disc to produce segmentation maps from the fundus input image; and a measurement module for computing region specific measurements within a standard zone within the fundus input image, and global physical or geometric measures of the whole fundus input image.

The present application relates to the technical field of computer image processing, in particular to a kind of retinal vessel density full-automatic partition measurement method;The present application uses the retinal color photo that ultra-wide-angle fundus camera is shot in multiple imaging modes, uses specific image processing method to combine it into three-dimensional matrix, uses improved convolutional neural network to identify three-dimensional matrix, realizes the full-automatic positioning of each area of retinal periphery, blood vessel identification, blood vessel density calculation;Meanwhile, it also makes up the vacancy of retinal vessel density full-automatic partition measurement based on ultra-wide-angle multi-imaging mode fundus camera, proposes the scheme of fine partitioning of retinal periphery according to field angle and an improved method of convolutional neural network fusing the features of multi-imaging mode image, to realize the full-automatic measurement of blood vessel density of each area of retinal periphery.

This invention proposes a retinal vessel segmentation network, segmentation method, and storage medium based on collaborative fusion and adaptive guidance. The dual-branch encoder includes a CNN encoder and a Transformer encoder, used to extract local detail features and global semantic features of retinal vessels, respectively. A collaborative fusion integrator connects the feature outputs of the CNN encoder and the Transformer encoder, used to achieve semantic alignment and fusion of local detail features and global semantic features through a convolution-based cross-attention mechanism. An adaptive feature guidance module connects the collaborative fusion integrator and the decoder, using feature enhancement and fusion based on spatial attention and cross-learning to preserve vascular detail information. The decoder restores the resolution of the features processed by the adaptive feature guidance module and outputs the retinal vessel segmentation result through classification. This invention achieves effective complementarity of dual-branch features, enhances multi-scale detail perception capabilities, and improves segmentation accuracy.

The invention relates to the technical field of medical image processing, in particular to a retinal blood vessel dynamic response evaluation system and method based on a graph neural network, and the method comprises the steps: a video obtaining system collects and preprocesses a blood vessel video of a to-be-detected target; the target identification and blood vessel tracking module receives the preprocessed video, completes feature extraction, target identification and detection, and determines a tracking target; the blood vessel diameter space-time dynamic estimation module receives the tracking target and estimates the blood vessel diameter in real time in combination with the blood vessel video; the blood vessel bifurcation point displacement spatio-temporal dynamic estimation module receives a tracking target and calculates a blood vessel displacement matrix by using a blood vessel pulsation segmentation algorithm and a blood pressure change estimation algorithm. The result display module receives the diameter of the blood vessel and the displacement matrix, displays an evaluation result, a spatial frequency domain blood vessel optical flow enhancement technology and a multi-scale structure tensor algorithm, reduces the measurement error of the diameter of the blood vessel from + / -5%-10% of a traditional method to + / -1%-3%, and can capture the dynamic change of the diameter of the micron-sized blood vessel.

The invention discloses a retinal blood vessel bifurcation angle determination method, system and device, a medium and a product, and relates to the field of medical image processing, and the method comprises the steps: carrying out the blood vessel segmentation of a retinal image, obtaining a binary image, and carrying out the fracture and cavity restoration to form a coherent image; respectively setting foreground and background pixels of the coherent image; traversing the coherent image, and deleting foreground pixels meeting a first set condition and a second set condition to obtain a blood vessel center line image; trimming the blood vessel center line image to obtain a blood vessel center line; based on the blood vessel center line and the calculation area, main blood vessels are selected in the supratemporal area and the subtemporal area respectively; and traversing the main blood vessel to obtain a blood vessel bifurcation point, and determining the diameter of the blood vessel, so as to determine the retina blood vessel bifurcation angle based on the blood vessel bifurcation point. According to the method, the influence of manual subjectivity can be effectively avoided, the determination efficiency is improved while the determination precision of the retinal blood vessel bifurcation angle is improved, and then the large-scale fundus screening requirement is met.

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.