107 results about "Retinal blood vessel" patented technology

An apparatus and method for safely cannulating a blood vessels, including but not limited retinal blood vessels, is described. In one embodiment, the apparatus can consist of a micropipette / microcannula, micromanipulator and positioner mounted to a base, which is attached to a wrist rest commonly used in eye surgery. The micropipette / microcannula is connected to tubing such that a medication may be injected through the micropipette / microcannula into the blood vessel or conversely, a small quantity of material may be removed from a blood vessel. Alternatively, a catheter, wire or stent may be placed through the micropipette / microcannula to treat or diagnose an area remote from the insertion site. The ability to cannulate a retinal blood vessel should be efficacious in the treatment of vein and artery occlusion, ocular tumors and other retinal, vascular and optic nerve disorders that would benefit from diagnosis and / or treatment. In another embodiment, a self-sealing needle is provided which allows the perforation of a blood vessel or other structure and the minimization of hemorrhaging when the needle is withdrawn from the blood vessel / structure. Another embodiment discloses an ocular implant needle.

Isolated, mammalian, adult bone marrow-derived, lineage negative hematopoietic stem cell populations (Lin− HSCs) contain endothelial progenitor cells (EPCs) capable of rescuing retinal blood vessels and neuronal networks in the eye. Preferably at least about 20% of the cells in the isolated Lin− HSCs express the cell surface antigen CD31. The isolated Lin− HSC populations are useful for treatment of ocular vascular diseases. In a preferred embodiment, the Lin− HSCs are isolated by extracting bone marrow from an adult mammal; separating a plurality of monocytes from the bone marrow; labeling the monocytes with biotin-conjugated lineage panel antibodies to one or more lineage surface antigens; removing of monocytes that are positive for the lineage surface antigens from the plurality of monocytes, and recovering a Lin− HSC population containing EPCs. Isolated Lin− HSCs that have been transfected with therapeutically useful genes are also provided, and are useful for delivering genes to the eye for cell-based gene therapy. Methods of preparing isolated stem cell populations of the invention, and methods of treating ocular diseases and injury are also described.

The invention relates to an attention mechanism U-shaped densely connected retinal (a novel retinal blood vessel segmentation method fusing DenseNet and Attention U-net network) blood vessel segmentation method, comprising the steps of retinal blood vessel image preprocessing and constructing a retinal blood vessel segmentation model. The method of the invention can effectively solve the problemsthat adjacent blood vessels are easy to connect, the micro blood vessels are too wide, the small blood vessels are easy to break, the blood vessel intersection is insufficient in segmentation, the image noise is too sensitive, the gray value of the object and the background is crossed, the optic disc and the lesion focus are missegmented, and the like. The method of the invention integrates a plurality of network models under the condition of low complexity, the excellent segmentation results are obtained on DRIVE dataset, the accuracy and sensitivity are 96.95% and 85.94%, respectively, whichare about 0.59% and 7.92% higher than those published in the latest literature.

In the context of the early detection and monitoring of eye diseases, such as glaucoma and diabetic retinopathy, the use of optical coherence tomography presents the difficulty, with respect to blood vessel segmentation, of weak visibility of vessel pattern in the OCT fundus image. To address this problem, a boosting learning approach uses three-dimensional (3D) information to effect automated segmentation of retinal blood vessels. The automated blood vessel segmentation technique described herein is based on 3D spectral domain OCT and provides accurate vessel pattern for clinical analysis, for retinal image registration, and for early diagnosis and monitoring of the progression of glaucoma and other retinal diseases. The technique employs a machine learning algorithm to identify blood vessel automatically in 3D OCT image, in a manner that does not rely on retinal layer segmentation.

Pre-treatment using a xanthin carotenoid (preferably 3R,3′R-zeaxanthin) can improve the benefits and efficacy of photodynamic therapy (PDT), which uses a light-activated drug (such as verteporfin) in patients who suffer from unwanted retinal blood vessel growth, including the “wet” (exudative) form of macular degeneration. Before a PDT treatment, patients are given a regimen of orally-ingested zeaxanthin for a period of at least 1 and preferably at least 2 to 3 weeks, at dosages of at least 3 and preferably at least 10 milligrams per day. Since zeaxanthin imparts a yellowish color to the macula, a preferred dosage should increase a patient's macular pigment density before the PDT treatment is performed.

A platform is proposed for automated analysis of retinal images, for obtaining from them information characterizing retinal blood vessels which may be useful in forming a diagnosis of a medical condition. A first aspect of the invention proposes that a plurality of characteristics of the retina are extracted, in order to provide data which is useful for enabling an evaluation of cardiovascular risk prediction, or even diagnosis of a cardiovascular condition. A second aspect uses fractal analysis of retinal images to provide vascular disease risk prediction, such as, but not limited to, diabetes and hypertension.

Systems and methods are described providing for the use of ultrasound energy to effect the dislodging of one or more blood clots inside blood vessels. Such clots can include those inside retinal vessels, especially in patients with central retinal vein occlusion. Embodiments of the present disclosure may be used for any retinal arterial or venous occlusion. In exemplary embodiments, a small probe can be inserted into the eye of a patient and placed over the retinal vessels. Acoustic streaming created by the probe can be directed to an area or region including targeted blood vessels, resulting in increased flow in one or more retinal veins and facilitating or effecting mechanical dislodging of one or more blood clots in the targets blood vessels. Exemplary embodiments can utilize ultrasonic energy produced at a frequency of approximately 44 MHz to 46 MHz with pulse repetition frequencies of approximately 100 Hz to 100 kHz.

The invention discloses an arteriovenous retinal blood vessel classification method based on an eye fundus image. The method includes the steps that first, a global blood vessel set and optic disk positioning information of the fundus image are acquired, the global blood vessel set is a set of all blood vessels in the fundus image, and the optic disk positioning information comprises the optic disk center of the fundus image; second, main blood vessels are determined according to the global blood vessel set and the optic disk positioning information and classified so that main blood vessel classification information can be obtained; third, the main blood vessel classification information is used for classifying the blood vessels in the global blood vessel set through a breadth first-search algorithm based on SAT so that global classification information can be obtained. According to the method, the classification information of the main blood vessels around an optic disk is first obtained, external expansion diffusion is performed from the main blood vessels through the breadth first-search algorithm based on SAT so that all the blood vessels can be obtained, a complete automatic blood vessel classification method is achieved, manual intervention is not needed, and classification precision is high.

The invention relates to the retinal blood vessel segmentation technology and especially relates to a multi-characteristic fusion monitored retinal blood vessel extraction method. The method comprises steps of step 1, retinal blood vessel image pre-processing; step 2, retinal blood vessel image characteristic extraction; step 3, random forest classifier training; and step 4, retina retinal blood vessel image post-processing. The method is advantaged in that through experiment verification of a DRIVE and STARE eyeground image database, susceptibilities are respectively 0.8354 and 0.8452, accuracies are respectively 94.82% and 95.34%, compared with existing retinal blood vessel segmentation methods in the prior art, the multi-characteristic fusion monitored retinal blood vessel extraction method is integrally better, moreover, disadvantages of the other methods at adjacent blood vessel portions, blood vessel cross sections and capillaries are solved, and segmented blood vessel structures are relatively closer to gold standards and actual blood vessel dimension values.

The invention provides a retinal blood vessel morphology quantization method based on a connected region. The method obtains a retinal blood vessel segmentation image after the fundus image is preprocessed, and then performs post-processing on the blood vessel segmentation image. On this basis, the vascular network is thinned and boundary treated, and the vascular centerline network and vascular boundary map are obtained. Corner detection is then performed and removed from the vascular centerline network so that the vascular segments of the vascular network form separate communication areas. Traversing is performed on the blood vessel segment, approximate the centerline of the blood vessel segments, and the blood vessel segment is changed into a broken line to calculate the direction of the blood vessel. At last, that initial diameter value is calculated, the center of the circle is selected by sliding on the centerline of the blood vessel segment, a semicircle window is created according to the direction of the circle cardiovascular and the diameter value measured in the early stage, and the distance between the window and the two intersection points of the blood vessel boundary is taken as a new diameter value. From this iteration, a set of vessel diameter values are measured, and the median value is the vessel diameter of the vessel segment. The invention is applicable to the quantification of large-scale retinal blood vessel morphology, and has high reliability.

The invention provides a fundus retinal vessel recognition and quantification method, device and equipment and a storage medium, and the method comprises the steps: inputting an original fundus imageinto a pre-trained U-shaped convolutional neural network model for processing, and obtaining a target feature map; performing optic disk segmentation based on the target feature map; segmenting the original fundus image to obtain an arteriovenous blood vessel recognition result; carrying out region-of-interest positioning based on the optic disk segmentation result; extracting a blood vessel center line according to the arteriovenous blood vessel recognition result, detecting key points in the blood vessel center line, removing the key points to obtain a plurality of mutually independent bloodvessel sections, and correcting arteriovenous category information on each blood vessel section; and based on the extracted blood vessel center line, obtaining the blood vessel diameter of each bloodvessel section after category information correction, and then quantifying arteriovenous blood vessels in the region of interest. According to the embodiment of the invention, the fundus retina artery and vein vessel identification precision is improved, and the quantization precision is further improved.

The invention provides a full-automatic retinal blood vessel analysis method and system suitable for intelligent portable equipment. The invention improves a saliency feature method by adding a preprocessing step which comprises performing homogenization on a fundus image and removing the central light reflection treatment of the blood vessel, thereby realizing rapid segmentation of the retinal blood vessel. The method has the advantages of good stability, simple operation and high speed. The invention realizes accurate analysis of various retinal blood vessels and realizes the combination with intelligent portable equipment by optimizing algorithm complexity and operation speed, so computer-aided diagnosis can provide systemic blood vessel analysis with strong applicability, stable calculation and high precision in retinal image analysis.

The invention discloses a method for enhancing blood vessels in retinal images based on the directional field, which includes: step one: the directional field Theta of a retinal image is calculated; step two: the blood vessels in the retinal image are enhanced according to the directional field Theta to obtain the enhanced image of retinal blood vessels; and step three: a Gabor wave filter is used for filtering noise in the enhanced image. The method can effectively enhance small blood vessels with low contrast ratio in the retinal images, which brings great convenience to the subsequent processing of the retinal images. In the invention, normalization processing is carried out to image backgrounds while the blood vessels are enhanced, which effectively overcomes influence of image noise and uneven lightness and saves the single normalization step. The invention has important practical value in the fields of dissection of blood vessels in the retinal and heart images and other fields.

A personal authentication method comprising imaging, on an image sensor as a laser speckle using an optical system, light reflected from retinal blood vessels of the ocular fundus and a blood vessel layer in ocular fundus internal tissue when a laser beam is expanded and made to irradiate the ocular fundus, calculating a quantity that represents the rate of change with respect to time of the amount of light received for each pixel of the laser speckle, obtaining an ocular fundus blood flow map as a two-dimensional map of the numerical values of the quantity, and comparison-checking against pre-registered personal data utilizing at least one, observed in the blood flow map, of blood flow distribution data, a pattern reflecting the course of retinal blood vessels, a pattern reflecting the course of blood vessels in ocular fundus internal tissue observed superimposed thereon, and data on changes thereof over time, and a device therefor. In accordance with the method and device of the present invention utilizing the ocular fundus blood flow rate map, a personal authentication method and device can be obtained that have remarkably higher accuracy than conventional methods and devices.

The invention discloses an eye fundus image arteriovenous retinal blood vessel classification method based on a breadth first-search algorithm. The method includes the steps that first, a global blood vessel set and optic disk positioning information of a fundus image are acquired, the global blood vessel set is a set of all blood vessels in the fundus image, and the optic disk positioning information comprises the optic disk center of the fundus image; second, main blood vessels are determined according to the global blood vessel set and the optic disk positioning information and classified so that main blood vessel classification information can be obtained; third, the main blood vessel classification information is used for classifying the blood vessels in the global blood vessel set through the breadth first-search algorithm based on SAT so that global classification information can be obtained. According to the method, the classification information of the main blood vessels around an optic disk is first obtained, external expansion diffusion is performed from the main blood vessels through the breadth first-search algorithm based on SAT so that all the blood vessels can be obtained, a complete automatic blood vessel classification method is achieved, manual intervention is not needed, and classification precision is high.

The invention relates to a non-fluorescent eye fundus image based automatic segmentation method for retinal blood vessels. The method comprises following steps: step one, an image is preprocessed, blood vessel characteristics are enhanced, and background noise is weakened; step two, morphological characteristics of the retinal image are extracted with a multi-scale linear operator, and a refined blood vessel framework is segmented with two-dimensional Gabor wavelet transform; step three, after the refined blood vessel framework is segmented, the image is subjected to path morphology filtering, the maximum path length is combined with geometrical characteristics of a blood vessel region, and an adjacent map is constructed; step four, a binaryzation process is performed with regional connectivity analysis and a hysteresis threshold technology for auxiliary segmentation of microvessels, and the segmentation is finished. Compared with the prior art, the method has the advantages as follows: the blood vessel location accuracy is higher, the adhesion phenomena are reduced, and a retinal blood vessel network of the eye fundus image is effectively segmented.

The present invention relates to a recombinant adenovirus expressing an αA-crystallin gene, and gene therapy for retinal vascular disease using the recombinant adenovirus. Gene therapy using the recombinant adenovirus comprising an αA-crystallin gene of the present invention increases the expression level of the αA-crystallin gene in the damaged retinal pericytes to suppress pericyte loss and death, retinal vascular leakage, and leukocyte adhesion surrounding retinal vessels, thereby protecting the pericytes. Therefore, it can be used for the prevention and treatment of various retinal vascular diseases including diabetic retinopathy.

The invention relates to a retinal blood vessel classification method and a retinal blood vessel classification device. The retinal blood vessel classification method combines a local binary algorithm with an independent component analysis technique for integrated use in the aspect of fundus blood vessel feature extraction, since the local binary algorithm and the independent component analysis technique performs structural description of blood vessel features from different angles, the method inherites the advantages of each single technology, supplements the blood vessels to certain extent, and filters the influence of more noise; and the retinal blood vessel classification method finally improves the precision of fundus blood vessel arteriovenous classification, reduces the proportion of blood vessels without classification marks, and provides a solid data basis for the follow-up diagnosis of diseases related to fundus blood vessels.

The invention belongs to the fields of diabetes mellitus and medicinal chemistry, and relates to the application of icariin or icarisid II in preparation of a medicine for preventing and curing diabetic retinopathy, in particular to the application of the icariin, an icariin-containing extract, the icarisid II or an icarisid II-containing extract in preparation of a medicine for preventing and / or curing the diabetic retinopathy. The icariin, the icariin-containing extract, the icarisid II or the icarisid II-containing extract have effective effects on repair or prevention and cure of the diabetic retinopathy, can improve the retinal vessel endothelial cell function remarkably, and can promote the reparative regeneration of optic nerve cells for diabetes mellitus.

The invention discloses a retinal blood vessel image editing method. The retinal blood vessel image editing method comprises the steps that a blood vessel annotation image of retinal blood vessels isacquired, wherein the blood vessel annotation image comprises blood vessel edge lines and blood vessel center lines, and anomaly annotation positions annotated on the blood vessels are obtained basedon the blood vessel annotation image; input information of a user is acquired, and corrected blood vessel center lines at the anomaly annotation positions of the blood vessels are determined based onthe input information; and corrected blood vessel edge lines at the anomaly annotation positions of the blood vessels are determined based on the corrected blood vessel center lines. The invention furthermore discloses a retinal blood vessel image editing device. Through the retinal blood vessel image editing method and the retinal blood vessel image editing device, the user can be assisted in realizing further editing on the retinal blood vessels just through simple operation easily and conveniently, the working efficiency of the user is improved, and meanwhile the accuracy of the retinal blood vessel image is improved.

The invention relates to a retinal blood vessel syringe, and particularly provides a retinal blood vessel syringe for an ophthalmologic operation robot. In order to solve the problems that the movement of the needle tip of an existing syringe device is limited, only linear translation and circumferential change can be achieved, consequently, a proper posture can be found by multi-time positioning,and the operation efficiency and the positioning efficiency are low, the retinal blood vessel syringe comprises an actuator fixing base (4), and further comprises a needle tube group module (1), a feeding module (2) and a rotating module (3), wherein the needle tube group module (1) is installed on the feeding module (2) and driven by the feeding module (2) to achieve feeding, the feeding module(2) is installed on the front portion of the rotating module (3), the needle tube group module (1) and the feeding module (2) are driven by the rotating module (3) to achieve rotation, and the rotating module (3) is installed on the actuator fixing base (4). The retinal blood vessel syringe is used on the ophthalmologic operation robot.

Isolated, mammalian, bone marrow-derived, lineage negative hematopoietic stem cell populations (Lin− HSC) contain endothelial progenitor cells (EPC) capable of forming retinal blood vessels. At least about 50% of the cells in the isolated Lin− HSC population include cell surface markers for CD31 and c-kit. Up to about 8% of the cells can include the Sca-1 cell marker, and up to about 4% of the cells can include the Flk-1 / KDR marker. The isolated Lin− HSC populations of the present invention are useful for treatment of ocular vascular diseases. The isolated Lin− HSC populations that have been transfected with therapeutically useful genes are also provided, which are useful for delivering genes to the eye for cell-based gene therapy.

The invention discloses a retinal vessel segmentation method and system based on a retinal fundus image, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected retinal fundus image; constructing a basic module according to the retinal fundus image features; cascading N basic modules to serve as a final network model, the to-be-detected retinal fundus image serving as input of the whole network model, and obtaining a segmentation result of retinal vessels. The foreground characteristics of the previous basic module and the originalpicture are transmitted to the next basic module together, so that the rear basic module can inherit the learning experience of the front basic module, the training process is accelerated, and the problem of data imbalance is effectively solved; the to-be-detected retinal fundus image is used as the input of the overall model S-UNet, and the obtained segmentation result of the retinal blood vessel is more accurate.

The invention relates to the field of biomedical image processing, in particular to a choroidal angiography method based on optical coherence tomography imaging body scanning. The method can be widelyused for screening and diagnosis of medical ophthalmic diseases. The method includes the steps of S1, acquiring original data of OCT body scanning fundus; S2, extracting retinal blood vessel shadow data and choroidal blood vessel data from the original data; S3, using the retinal blood vessel shadow data for masking the choroidal blood vessel data to obtain choroidal angiography data. According to the technical scheme above, by extracting the retinal blood vessel shadow data to mask the choroidal blood vessel data, retinal blood vessel shadows in the choroidal blood vessel data are removed, so that the influence of the retinal blood vessel shadows on the contrast and continuity of choroidal blood vessel projection images is reduced, and choroidal vessels can be completely and clearly displayed.

The invention discloses a retinal vessel artery and vein distinguishing method. The method comprises: obtaining a blood vessel extraction image, a fundus image and optic disc center coordinates; extracting a main blood vessel according to the blood vessel extraction image, the fundus image and the optic disc center coordinate; the method comprises the following steps: obtaining a main blood vesselimage, intercepting a main blood vessel based on the main blood vessel image to obtain a plurality of single blood vessel sections, performing pipe diameter measurement on the plurality of single blood vessel sections to obtain the pipe diameter width of each single blood vessel section, and selecting and obtaining a plurality of arteriovenous blood vessel pairs according to the pipe diameter widths. The method is advantaged in that the test effect on the fundus retina color image of the actual scene is excellent, relatively good effects are realized for fundus images of different types, different brands and different levels of image quality, algorithm robustness and universality are relatively strong, and possibility is provided for retina vessel arteriovenous distinguishing and tube diameter measurement algorithm actual scene landing.

Disclosed is a method for measuring the retinal blood vessel diameter and thickness of a vessel wall. The method is characterized by including: adopting an SD-OCT (spectral domain optical coherence tomography) scanning system for measurement and selecting all the retinal blood vessels in an area with the distance apart from the edge of an optical disc 0.5-1.0 times of the diameter of the optical disc as measuring objects through the SD-OCT system; allowing a scanning line to be perpendicular to the running direction of a positioning blood vessel to acquire a cross section figure of the retinal blood vessels; magnifying the scanned cross section figure of the blood vessels; moving a scale ruler to measure inner diameter and outer diameter of each blood vessel, wherein the thickness of the vessel wall=(the outer diameter-the inner diameter) / 2; calculating the central retinal artery equivalent Wt; taking the central retinal artery equivalent Wt=(0.87Wa<2>+1.01Wb<2>-0.22WaWb-10.73)<1 / 2> as a diagnostic basis. The method has the advantages of being free of damage, non-invasive, free of mydriasis and high in measurement precision.

The invention relates to the technical field of image analysis, health service and data processing, and provides a method for analyzing the hypertension retinal vessel change characteristic data. Themethod includes: obtaining the fundus image of the patient; extracting and identifying the retinal vessel change characteristic data of a fundus image of a patient, wherein retinal vessel change characteristics comprise limitation retinal artery constriction; analyzing and comparing the retinal vessel change characteristic data of the patient in different periods; further obtaining the change condition of the fundus screening characteristic data of the patient; analyzing blood pressure control of the patient in a near period and related prevention and treatment effect conditions of hypertension. A powerful excitation mechanism is provided for enhancing the compliance of basic treatment intervened by the lifestyle of a patient, a user is greatly helped to detect and control the blood pressure consciously and regularly, and the method has important significance in treatment effect evaluation and chronic disease management of hypertension.

The invention discloses a retinal arteriovenous classification method based on topological structure estimation. The method comprises the steps of performing pixel-level segmentation on a retinal vessel image through a U-net deep learning network to obtain vessel segmentation result information; separately carrying out topology estimation on the blood vessel segmentation result information throughan advantage set theory, establishing a complete topology tree set, and obtaining topology information; carrying out arteriovenous classification on the myopia disc area blood vessels through the advantage set theory, and obtaining a myopia disc area blood vessel arteriovenous classification result; and processing the topological information and the arteriovenous classification result of the myopia disc region blood vessels according to a label propagation algorithm to obtain global arteriovenous classification. The invention further discloses a retinal arteriovenous classification device based on topological structure estimation. According to the invention, the topology of retinal vessels is continuously added into arteriovenous classification work, and the arteriovenous problem of vessels in a far optic disc area in fundus color photograph can be effectively solved through topology information guidance.