50 results about "Nerve fiber" patented technology

The invention relates to the technical field of medical image recognition, in particular to an ultrasound-guided nerve block liquid medicine diffusion image recognition method, which comprises the following steps of: dynamically analyzing a set with external expansion morphological feature points on a liquid medicine diffusion boundary to construct directional data representing a diffusion trend, and performing density statistics on the directional data to obtain an image recognition result; therefore, the dominant direction of liquid medicine diffusion in space is accurately identified, judgment is carried out in combination with the change trend of the dominant direction in a continuous time frame, a time slice with the stable and continuous direction is screened out, then the method goes deep into a liquid medicine coverage area, the consistency between the pixel gray scale gradient change and the nerve fiber anatomy trend is compared, and the accuracy of the nerve fiber anatomy is improved. Finally, effective classification of the diffusion paths is realized, an effective diffusion area conforming to the trend of the neural structure and a non-dominant diffusion area towards other tissues can be clearly distinguished, and the accuracy and clinical reference value of an identification result are greatly improved.

The invention discloses a glaucoma optic nerve fiber layer dynamic tracking method and system, and relates to the field of medical image analysis, and the method comprises the steps: obtaining visual field examination data and OCT scanning data; calculating a defect change value and an optic nerve fiber layer thickness change value; the thickness change value and the defect change value are paired to form a joint change data set; judging the state of the sampling point, and determining the sampling point as a joint progress area, a structural risk area or a functional risk area; analyzing the dynamic change trend of each region, and evaluating the progress risk level; calculating a time sequence correlation metric value and a time delay parameter between the thickness change sequence and the defect change sequence to form a space coupling strength distribution diagram; adjusting the boundary of the risk area based on the distribution map, and marking a dynamic boundary adjustment area; and dynamically adjusting the density and the frequency of the OCT sampling points in the next period according to the adjustment area and the coupling strength distribution result. According to the invention, accurate monitoring and early warning of glaucoma conditions are realized.

The present application relates to the technical field of medical image processing, in particular to an image intelligent analysis system for corneal nerve fibers of dry eye patients, which comprises an image acquisition module, an image preprocessing module, a nerve fiber segmentation module, a nerve morphological analysis module based on differential geometry, a nerve regeneration monitoring module, a data management module and a clinical auxiliary decision module. The nerve morphological analysis module based on differential geometry is the core innovation, which contains a nerve fiber curvature analysis unit, a multi-scale differential invariant extraction unit and a nerve network topology structure quantification unit. The system introduces the theory of differential geometry, represents the nerve fiber through a parameterized curve and calculates the local curvature characteristics. A Gaussian scale space is constructed to extract multi-scale differential invariants. The graph structure representation of the nerve network is established to analyze the topological characteristics. The morphological characteristics of the corneal nerve fibers can be quantified comprehensively from the local curvature to the global topological structure, and the accurate detection of the slight changes of the nerve fibers is realized.

The invention relates to the technical field of biomedical engineering, and discloses a neural conduction pathway quantitative analysis method and system based on a neural potential waveform. The system comprises: a bioelectric signal acquisition module configured to establish a time sequence characterizing the change of neural current over time; the waveform deep learning module is configured to fit conduction waveforms of the A beta nerve fiber, the A delta nerve fiber and the C-type fiber by using a deep learning algorithm based on the time sequence, and identify a peak point of the conduction waveform of each fiber; the quantitative analysis module is configured to calculate the frequency and intensity function of each fiber conduction signal according to the peak point, and the physiological characteristic parameters comprise the myelination degree, the conduction speed and the sensitivity to anesthetics; and the control module is configured to generate a differential blocking strategy aiming at the A beta nerve fiber, the A delta nerve fiber and the C-type fiber based on the frequency and intensity functions and generate the anesthetic dosage. And specific recognition and differential blocking control of A beta, A delta and C type fiber signals are realized.

The application discloses a composition with soothing anti-inflammatory anti-wrinkle firming efficacy and a preparation method thereof, and the composition comprises a TRPV1 inhibitor, an extract of Glycyrrhiza inflata Bat., palmitoyl tripeptide-1 and palmitoyl tetrapeptide-7. The four raw materials are compounded to have a synergistic effect, reduce the release of pro-inflammatory mediators and inhibit the high reactivity of nerve fibers to achieve soothing anti-inflammatory and anti-irritation; promote the generation of self collagen and sodium hyaluronate, reduce the oxidative stress response to achieve the anti-wrinkle firming efficacy and delay skin aging. The composition has comprehensive effects in soothing, anti-inflammatory, anti-irritation, anti-oxidation, anti-wrinkle and firming, reduces the generation of inflammation and delays skin aging, relieves the burning and stinging sensation of the skin at the same time, and meets the requirements of safety and high efficiency pursued by sensitive skin groups.

The application belongs to the field of brain white matter fiber track prediction, and relates to a brain white matter fiber track prediction method and system based on a pre-trained basic model. The method adopts an MAE architecture, and performs self-supervised pre-training on large-scale brain fiber track data through a mask-reconstruction mechanism to learn global topology and local geometric features of neural fibers. In the training stage, a hybrid progressive mask strategy is introduced. In the initial stage of the model, continuous segment masks are mainly used, and gradually transition to random point masks, so as to realize hierarchical learning of structural patterns and robust expression of features. After pre-training is completed, the model is fine-tuned, so that only the input of the two end point coordinates of the fiber can reconstruct and predict the intermediate track, and the fiber path inference based on the end point is realized. In the fine-tuning stage, the encoder structure is kept stable, the decoder is guided by the end point features to generate complete tracks conforming to the structural distribution of the brain white matter fiber, and the technical problems existing in the prior art are solved.

The invention discloses a brain tumor potential recurrence risk area determination device and a model training method, and relates to the field of medical science and technology and the field of artificial intelligence. The device comprises a delineation determination unit used for determining a brain tumor delineation area based on a target medical image of a target object; the nerve fiber reconstruction unit is used for simulating and reconstructing image information of N target nerve fibers of the target object according to the target medical image; and the risk area prediction unit is used for inputting the target medical image and the image information of the N target nerve fibers into the target model, performing feature extraction on the received information through the target model, and determining a recurrence risk area of the brain tumor of the target object according to the extracted features by using priori knowledge in a model training stage. According to the method and the device, the technical problem of low accuracy of determining the potential recurrence risk area of the brain tumor caused by judging the risk diffusion condition of the brain tumor only through imaging observation or expert experience in the prior art is solved.

The application discloses a preparation method and application of a nerve conduit with a multistage biomimetic nerve membrane structure and belongs to the technical field of tissue engineering materials. The nerve conduit with the multistage biomimetic nerve membrane structure comprises a biomimetic inner membrane support with a secondary guiding structure and a biomimetic outer membrane support with a double-layer structure. In the nerve conduit with the multistage biomimetic nerve membrane structure, the filler for playing a growth guiding role has a multistage guiding structure, the primary guiding is that cells are directionally migrated along the fiber direction in the directional fiber membrane, are elongated in shape on the fiber and are accelerated in differentiation, and the secondary guiding is that directional microchannels constrain the growth of nerve fibers, promote the maturation of bundling and the alignment of the growth direction. The application solves the problem of insufficient contact guiding efficiency existing in the prior art and overcomes the contradiction between the filling density of guiding fibers, the growth space and the guiding effect.

The invention discloses a brain image VR system developed based on unity and blender. The system comprises a nerve fiber direction sensing coloring module, a multi-brain structure model intelligent splitting and memorizing module, a double-data-set layered precise control module, an intelligent profile generation module, a double-end adaptive intelligent slicing module and a customized VR immersive interaction module. The invention further provides a brain image VR method based on unity and blender development. According to the method, the display and rendering efficiency problem of the brain image OBJ model in Unity is effectively solved; the comfort of model observation in the VR scene is improved; and the adaptation stability of the system to different brain image data is ensured.

The invention discloses a medical clinical image real-time 3D imaging method fused with artificial intelligence, and the method comprises the steps: extracting a basic feature set of original data of a medical clinical image through a real-time 3D image computing power engine, and completing hierarchical feature aggregation through a multi-scale feature aggregation segmentation model to generate a multi-dimensional aggregation feature map, the method comprises the steps of locking focus edge coordinates and drawing an initial contour by means of a focus boundary accurate positioning model, obtaining space correlation data of a focus and a nerve fiber bundle through a nerve fiber bundle three-dimensional tracking algorithm, performing parallel operation, three-dimensional reconstruction and fusion on various types of data by utilizing a real-time 3D image computing power engine, and finally outputting a 3D imaging result through dynamic rendering. And through multi-model collaboration and computing power optimization, full-process intelligentization of feature extraction, focus positioning, structure tracking and imaging output is realized, and the accuracy and real-time performance of imaging are improved.

The present invention is directed inter alia to the treatment of pain. For example, there is provided a chimeric clostridial neurotoxin for use in treating pain by inhibiting the release of a pain mediator from a neuron comprising an Aδ nerve fiber or a C nerve fiber, wherein the chimeric clostridial neurotoxin binds to the neuron comprising the Aδ nerve fiber or the C nerve fiber, respectively, and wherein the chimeric clostridial neurotoxin comprises a botulinum neurotoxin A (BoNT / A) light-chain and translocation domain (HN domain), and a BoNT / B receptor binding domain (HC domain). Also provided are methods, uses, kits, and unit dosage forms.

The invention relates to a compound indoleacetic acid and application of the compound indoleacetic acid as an active ingredient in preparation of medicines or foods for treating optic nerve injury, and the indoleacetic acid is given in vivo, so that the number of retinal ganglion cells and the thickness of a retinal nerve fiber layer are favorably maintained, and activation of retinal microglial cells is inhibited; retina inflammation pathway activation and inflammatory mediator expression are inhibited, so that the visual function is protected.

The present invention is directed inter alia to the treatment of pain. For example, there is provided a chimeric clostridial neurotoxin for use in treating pain by inhibiting release of a pain mediator from a neuron comprising an Aδ nerve fiber or a C nerve fiber, wherein the chimeric clostridial neurotoxin binds to the neuron comprising the Aδ nerve fiber or the C nerve fiber, respectively, and wherein the chimeric clostridial neurotoxin comprises a botulinum neurotoxin A (BoNT / A) light-chain and translocation domain (HN domain), and a BoNT / B receptor binding domain (HC domain). Also provided are methods, uses, kits, and unit dosage forms.

To provide an artificial device that resembles the network of nerve fibers in biology.SOLUTION: Nerve fibers extending from the human brain cells have fractured surfaces called synapses and form a small electric current called an impulse in the gaps generated at the fractured surfaces. As this formation is repeated at the synapses, the small electric current of the impulse is combined and increases in power. As a result, the brain cells continue to secure an infinitely stable power source. This fact certainly exists. In other words, the brain's power source development continues until the human dies, until the heart stops, and, paradoxically, if a human does not die, the power source will continue to be secured forever. How is the electric current that constitutes this power source created in the brain. Focusing on the structure of the nerve fibers of the brain, an artificial device similar to the network of nerve fibers in biology has been realized by applying engineering and physics to the study.SELECTED DRAWING: Figure 1

A computer system that computes second tractography results is described. This computer may include: a computation device (such as a processor, a graphics processing unit or GPU, etc.) that executes program instructions; and memory that stores the program instructions. During operation, the computer system receives information specifying tractography results that specify a set of neurological fibers. Then, the computer system computes, using a predetermined (e.g., pretrained) autoencoder neural network, the second tractography results that specify a second set of neurological fibers based at least in part on the tractography results and information associated with a neurological anatomical region. For example, a subset of the set of neurological fibers may be anatomically implausible and the second set of fibers may exclude the subset. Note that the predetermined autoencoder neural network may be trained using an unsupervised-learning technique.

The invention provides a cranial nerve fiber bundle labeling method and device, electronic equipment and a readable storage medium, and relates to the technical field of image processing.The cranial nerve fiber bundle labeling method comprises the steps that original spectrograph imaging data containing a brain image is collected, and the original spectrograph imaging data is solved to obtain an intensity image and a phase delay image; respectively determining to-be-labeled data in the intensity image and the phase delay image after noise reduction preprocessing, segmenting the to-be-labeled phase delay image based on a coarse segmentation threshold value obtained through pre-calculation to obtain a coarse segmentation mask, and on the basis of an initial brain nerve fiber bundle form determined by the coarse segmentation mask, marking the to-be-labeled data in the intensity image and the to-be-labeled data in the phase delay image; obtaining labeling results of the brain nerve fiber bundle of the to-be-labeled intensity image of different users, and storing the labeling results under the condition that similarities among different labeling results of all labeling layers in the labeling layer range are greater than a specified threshold value; and the cranial nerve fiber bundle can be positioned and labeled more accurately by utilizing complementarity information of the intensity image and the phase delay image.

The invention discloses a retina nerve fiber layer defect analysis method based on an eye fundus image, and relates to the technical field of retina nerve fiber layer image recognition and analysis. The technical defects that fiber defects of a retina nerve fiber layer are high in cost, analysis depends on experience, and quantization and accurate classification capacity is lacked are overcome. The method comprises the steps that 1, image data are acquired; the method comprises the following steps: acquiring an eye fundus image of a retina nerve fiber layer through an eye fundus camera, acquiring first scale image data, acquiring second scale image data of which the pixel value is greater than that of the first scale image data, and marking a nerve fiber most concentrated area on the second scale image; 2, generating a dual-scale supervision signal; step 3, data enhancement processing; 4, constructing a multi-task deep learning model; step 5, defect classification; the feature map is converted into a feature vector through a global average pooling layer, and then a classification prediction result is obtained through two full connection layers. The problem of dependence on subjective experience of doctors is effectively solved, and the diagnosis efficiency is improved.

The present application provides an eye state evaluation method and an electronic device. The method comprises: obtaining an optic disc image region from a first fundus image, and generating a plurality of optic cup disc ratio evaluation results based on the optic disc image region by a plurality of first models; obtaining a first evaluation result of the eye based on the optic cup disc ratio evaluation results; performing a plurality of data augmentation operations on the first fundus image to generate a plurality of second fundus images; generating a plurality of optic nerve fiber layer defect evaluation results based on the second fundus images by a plurality of second models; obtaining a second evaluation result of the eye based on the optic nerve fiber layer defect evaluation results; and obtaining an optic nerve evaluation result of the eye based on the first evaluation result and the second evaluation result. Thus, the present application can provide a reference for doctors when evaluating the eye state of patients, thereby helping doctors to give appropriate evaluation results for the eyes of patients.

Magnetic resonance methods comprise tractographically establishing a path along a structure in a specimen and finding a distribution of structure radii or cross-sectional areas along the path. Based on the distribution and the path, end-to-end functional characteristics of the structure are estimated. For example, nerve transit times or distributions of transit times can be estimated for a plurality of nervous system locations such as Brodmann areas. Comparison of estimated transit times or distributions thereof between reference values or other values from the same structure can be used to assess specimen health.

The present invention discloses a method, device and equipment for identifying retinal nerve fiber layer defects, wherein the method includes: obtaining an eye image to be identified, including: retinal nerve fiber layer information; performing image recognition on the eye image to be identified based on the retinal nerve fiber layer information to obtain a first identification area, the first identification area including at least two first feature points, each first feature point being used to characterize a retinal nerve fiber defect starting point; selecting any first feature point for parameter configuration to obtain an identification feature; based on the identification feature, performing sub-feature identification on the at least two first feature points respectively to obtain at least two sub-feature curves, each sub-feature curve being used to characterize the area of ​​retinal nerve fiber defects corresponding to the first feature point; obtaining a target identification area based on the at least two sub-feature curves, the target identification area being the retinal nerve fiber layer defect area. In the above manner, the present invention improves labeling efficiency and quality.

Various aspects of this disclosure relate to a layered polymer substrate with electrodes and conductive ink embedded in the substrate. In some embodiments, a prosthetic liner may be bonded onto the polymer substrate. The substrate may include an interconnect coupled to the electrodes via the conductive ink. The system may include a controller (e.g., an electrode controller) in communication with the electrodes via the conductive ink. The electrodes may be activated such that they may stimulate nerve fibers in a user's residual limb.

The application discloses a multifunctional underwater sensing fiber bionic nerve hillock cell receptor and a preparation method, and belongs to the field of bionic artificial receptors. The bionic nerve hillock cell receptor comprises a bionic nerve fiber, a first reflecting surface, a bionic supporting cell, a second reflecting surface, a bionic sensory hair cell and a bionic gel cap. The bionic nerve fiber is used for transmitting input light signals and output light signals. The bionic supporting cell is used for supporting the bionic sensory hair cell and the bionic gel cap, and forms a Fabry-Perot cavity together with the first reflecting surface and the second reflecting surface. The bionic sensory hair cell comprises one moving cilium and a plurality of non-moving cilia. The bionic gel cap contains bionic gel material, has viscous resistance and friction, and is used for wrapping the bionic sensory hair cell. The application solves the problems of poor biocompatibility, large volume, poor directivity and single function of the bionic receptor in the prior art.

The invention relates to the technical field of visual impairment simulation, in particular to a visual impairment dynamic evolution simulation system and method. Multi-modal medical image data of a patient is collected and subjected to preprocessing and focus positioning, an individual visual pathway fiber bundle is constructed by adopting a deterministic fiber tracking algorithm based on diffusion tensor imaging data, non-rigid registration is performed on the individual fiber bundle and a standard visual pathway template, a local deformation field is calculated, and the focus positioning accuracy is improved. Based on a local deformation field, analyzing a spatial geometrical relationship between a focus mask and each section of nerve fiber to obtain an individualized three-dimensional visual pathway model and an SDI scoring matrix of each section corresponding to the individualized three-dimensional visual pathway model, and judging a damaged pathway section and a typical visual field defect mode matched with the damaged pathway section in degree; according to the method, individualized visual simulation of a patient is realized, a visual dynamic evolution prediction model is constructed by utilizing diffusion Transform, and dynamic evolution of visual impairment is realized.

1. Name of this design product: Graphical user interface for visual remote management of electronic equipment. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this design product lies in the graphical user interface content on the screen. 4. The picture or photo that best illustrates the design points: Interface change state diagram 2. 5. Other views are of conventional design and are omitted. 6. Purpose of the graphical user interface: The interface of this design product is a graphical user interface that assists doctors in visually and remotely managing the electric field range of the patient's brain nuclei, electrode contacts, and stimulation parameters. 7. Human-computer interaction mode of the graphical user interface: The main view is the remote control interface after connecting the pulse generator (IPG). The left side of the interface is the patient's video interface, the lower right corner of the left side is a small 3D model window, which displays the patient's 3D model, and the right operation bar is the selectable electrode contact button and stimulation parameter button. The interface usage status reference Figure 1 is the interface usage status reference diagram of the main view; in the main view, after clicking the minimize button in the upper right corner of the 3D model window, the 3D model window will be hidden, and the interface change status diagram 1 will be entered. The interface usage status reference Figure 2 is the interface of the interface change status diagram 1 Interface usage status reference diagram; in interface change state diagram 1, click the 3D icon button in the lower right corner of the patient's video interface to restore the three-dimensional model window, and click the maximize button in the upper right corner of the three-dimensional model window to enter interface change state diagram 2. Interface usage status reference diagram 3 is the interface usage status reference diagram of interface change state diagram 2; in interface change state diagram 2, click the nerve fiber button in the lower left corner of the three-dimensional model window, and click the select all button and the union button to enter interface change state diagram 3. Interface usage status reference diagram 4 is the interface usage status reference diagram of interface change state diagram 3.

The embodiment of the invention discloses a pulse modulation parameter determination method and device, equipment and a medium. The method comprises the following steps: determining an electric field region generated on a preset target spot by a first pulse modulated according to a preset pulse parameter based on an implantation position of an electrode lead in the implantable medical system on the preset target spot; determining a target nerve fiber activated by the electric field area; based on a preset activation condition corresponding to the target nerve fiber, performing modulation processing on the preset pulse parameter to obtain a target pulse parameter; wherein the electric field area generated by the target pulse parameter does not activate at least one target nerve fiber, and the preset activation condition is a parameter condition required to be met by the pulse for activating the target nerve fiber. According to the technical scheme provided by the embodiment of the invention, the condition of abnormal nerve impulse after the pulse corresponding to the target pulse parameter stimulates the nerve fiber is avoided, and the working accuracy and safety of the implantable medical system are improved.

Various aspects of this disclosure relate to a layered polymer substrate with electrodes and conductive ink embedded in the substrate. In some embodiments, a prosthetic liner may be bonded onto the polymer substrate. The substrate may include an interconnect coupled to the electrodes via the conductive ink. The system may include a controller (e.g., an electrode controller) in communication with the electrodes via the conductive ink. The electrodes may be activated such that they may stimulate nerve fibers in a user's residual limb.

The application provides a brain nerve fiber bundle segmentation and reconstruction method and device and electronic equipment, relates to the technical field of image processing, and comprises the following steps: obtaining spectrometer imaging data containing brain images and corresponding brain nerve fiber bundle labels; slicing the calculated spectrometer imaging data into two-dimensional data; dividing the two-dimensional data into training data and test data according to a specified proportion; taking the training data as sample training data and the corresponding brain nerve fiber bundle labels as labels; training an initial segmentation network based on a target loss function to obtain a brain nerve fiber bundle segmentation model; inputting the test data into the brain nerve fiber bundle segmentation model to obtain a predicted brain nerve fiber bundle segmentation image; stacking the predicted brain nerve fiber bundle segmentation image to generate a three-dimensional image block; and splicing the three-dimensional image block by using the information of the overlapping area between adjacent three-dimensional image blocks to complete three-dimensional reconstruction of the brain nerve fiber bundle. The application provides an efficient and accurate brain nerve fiber bundle segmentation and three-dimensional reconstruction method.

The invention discloses a DT I-ALPS mapping graph generation method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a medical image, carrying out the fiber mapping calculation of the medical image, and obtaining a fiber diffusivity value; performing region-of-interest positioning processing on the medical image to obtain a reference region-of-interest; according to the fiber diffusivity value and the reference region of interest, performing three-dimensional voxel-by-voxel calculation processing on nerve fibers in the medical image to obtain DT I-ALPS indexes of different spatial resolutions; and performing pseudo-color display processing on the medical image according to the DT I-ALPS index to obtain a mapping graph. According to the embodiment of the invention, the DT I-ALPS index can be displayed more visually, and the method can be widely applied to the technical field of image processing.

An electrohydraulic shock wave generator includes a capacitor configured to store electrical energy and a discharge circuit configured to discharge the capacitor through an electrode assembly positioned in an electrically conductive medium to generate shock waves. A diode suppression network is positioned within the discharge circuit and configured to prevent reverse voltage transients and suppress discharge oscillations. The diode suppression network converts underdamped oscillatory discharge into critically damped discharge waveform to produce shock waves having narrow pulse widths. The generator may include low-resistance discharge circuits with conductors having greater cross-sectional areas and total circuit resistance of less than 0.025 ohms. The capacitor may have a capacitance of 25 nanofarads or less and store electrical energy at voltages between 15 kilovolts and 60 kilovolts. The critically-damped discharge waveform produces shock waves having rapid rise times that minimize activation of pain-sensing nerve fibers during therapeutic treatment.