51 results about "Brain atlas" patented technology

Morphing or fitting a brain atlas to a diagnostic image data set of the patient, or to a patient registered to the brain atlas, is enhanced using measurements of one or more physical characteristics of the brain taken with the aid of an instrument as it is being inserted into the brain. The measurements are then compared against known physical characteristics of certain brain structures, thus permitting correlation of the measured physical characteristic to a brain structure. The brain atlas then may be morphed or deformed so that the identified brain structure in the atlas is at or near the position at which the measurement was taken, as known from the tracked position of the instrument.

Brain images are processed and analyzed with the aid of a computer for stroke diagnosis or therapeutic decision making, where multiple stroke-related images are superimposed. The superimposed images include brain images that have infarct and penumbra regions, and patient-specific brain atlas images. The infarct and penumbra regions are determined and delineated on the superimposed images. Each patient-specific brain atlas image may be formed by mapping a pre-existing brain atlas to a co-ordinate system in which the brain images are co-registered. A brain atlas may depict brain structures such as anatomy structures, blood supply territories (BST), or cerebral vasculature. The superimposed images may be used to determine any overlap between a particular brain structure and the infarct and penumbra regions.

Methods for fully automatic quantification and interpretation of three dimensional images of the brain or other organs. A system for Computer Aided Diagnosis (CAD) of diseases affecting cerebral cortex from SPECT images of the brain, where said images may represent cerebral blood flow (CBF). The methods include image processing, statistical shape models, a virtual brain atlas, reference databases and machine learning.

The invention relates to a mixed brain white matter nerve fiber automatic cluster and a marking method. The marking method is characterized in that: firstly, using a high dimensional elastic registration method to match a standard brain atlas to a single brain space, thereby being capable of dividing a single brain into 104 anatomical regions with structural information; secondly, using differentnerve fiber bundles to extract and mark 15 nerve fiber bundles according to the characteristics of different brain anatomical regions; thirdly, to other 4 nerve fiber bundles which can not be extracted by the above method due to strong winding between the 4 nerve fiber bundles and the nerve fiber bundles at the periphery thereof, using a method combing Kernel-PCA and Fuzzyc-mean and based on similarity measurement to automatically carry out clustering analysis; and at last, using an identification method based on characteristics to mark the result of clustering analysis. By the above method, 19 main white matter nerve fiber bundles of the brain can be extracted and marked automatically.

The invention relates to an electrophysiology recording device which is used for simultaneously recording electrophysiological signals of multiple brain regions of a laboratory animal. Through holes with the same number as the brain regions of the to-be-recorded electrophysiological signals are formed in a positioning plate; the through holes distributed in the positioning plate correspond to the to-be-recorded brain regions determined by a brain atlas of the laboratory animal one to one; recording electrodes run through the positioning plate by virtue of the through holes; the parts, positioned in the through holes, of the recording electrodes are butted with the positioning plate. According to the electrophysiology recording device, multiple recording electrodes can be simultaneously transplanted into multiple brain regions so as to record the electrophysiological signals of the multiple brain regions.

The invention relates to a brain slice image region division method and device, and belongs to the technical field of image registration. The method comprises the steps of acquiring multiple groups ofimages wherein each group of images comprises an original image to be subjected to region division and a gray scale and color standard brain atlas corresponding to the original image; performing affine transformation on the gray scale and color standard brain atlas to obtain a gray scale and color standard brain atlas after affine transformation; respectively carrying out feature extraction on the original image and the affine-transformed gray scale standard brain atlas to obtain a single-mode original image and a single-mode gray scale standard brain atlas; taking the single-mode original image and the single-mode gray scale standard brain atlas as training samples, and training a registration network; inputting the color standard brain map after affine transformation into a trained registration network to obtain a registered color standard brain map; and fusing with an original image to complete region division. Full-automatic and rapid region division of the multi-modal brain sliceimage is realized, the operation is simple and convenient, and the region division is accurate.

The invention is applicable to the technical field of medical images, and provides an image-driven brain atlas construction method and device, equipment and a storage medium. The method comprises the steps of obtaining multi-modal data of a to-be-predicted brain, wherein the multi-modal data is obtained according to image data collected by the brain in at least three different modes; inputting the multi-modal data into a preset fusion network for processing, and outputting to obtain characteristic parameters of the brain; the processing of the multi-modal data by the fusion network comprises the steps of extracting non-Euclidean spatial features and Euclidean spatial features of the multi-modal data, and performing hypergraph fusion on the non-Euclidean spatial features and the Euclidean spatial features to obtain feature parameters which are used for representing a brain connection matrix and/or a disease category of the brain. FEatures of different spaces are mapped into a hypergraph data structure for hypergraph fusion, possible isomerism among the features of the different spaces is avoided, and therefore complementary fusion features among the multi-modal data are obtained.

The invention discloses a laterality detection method for a brain structure network based on module connection, and the method comprises the following steps: carrying out the preprocessing of a diffusion tensor image, and carrying out the region segmentation of the preprocessed diffusion tensor image according to a selected standardized brain atlas; adopting a deterministic fiber bundle tracking algorithm; reconstructing fibers of the whole brain on the basis of tracking end conditions, and calculating the number of fiber bundles and partial anisotropy indexes of every two brain regions and the superficial area of each brain region to obtain a fiber bundle number matrix FN, a partial anisotropy index matrix FA and a brain region superficial area matrix Surface of the brain regions. Compared with a traditional laterality detection method, the laterality detection method of the brain structure network based on module connection has the advantages that the modularity and the laterality ofthe network are fused, the laterality of local network indexes of the brain can be mined more effectively and accurately, and certain help is provided for exploring a brain working mechanism.

The invention relates to a segmenting method of a gecko brain atlas processed through hematoxylin-eosin (HE) staining. Aimed at color features of tissues on the brain atlas shot by a Microscope or a digital camera, the segmenting method based on an YCbCr space is provided. According to the segmenting method of the gecko brain atlas processed through the HE staining, firstly, the color of the atlas is converted from an RGB space into the YCbCr space, then the brain atlas is segmented according to distribution differences of components Cb and components Cr in different regions of the atlas, and finally the different tissues in the atlas are completely segmented. The segmenting method provides a more convenient condition for researches on the brain structures of geckos and corresponding biobots.

The invention relates to a method for establishing an animal model for senile dementia, a special liquid medicine and a dosing device, and belongs to the technical field of neurosciences. The method comprises the following steps: obtaining positioning coordinates of paraceles according to brain atlases or/and magnetic resonance image data of an experimental animal; respectively burying a conduit of the dosing device in left and right paraceles of the animal, and injecting the special liquid medicine to the paraceles at a constant speed in 14-16 minutes under a sterile environment within 2-3 weeks, wherein the liquid medicine amount is 100-250 ml methanol per day; alternately dosing the left and right paraceles, wherein the dosing frequency is 18-30 hours at an interval; and continuously dosing for 2-10 months to obtain a non-human primate animal model for senile dementia. With the adoption of a novel dosing mode: dosing to the paraceles at the constant speed, medicine can circulate to whole brain through the cerebrospinal fluid to exert the toxic effect, so that peripheral damage by the medicine is avoided. The AD model is established by injecting methanol or formaldehyde to the paraceles, so that behaviors and pathological symptoms are typical, the basic physiological status is good and the individual difference is relatively smaller.

A method for establishing a non-human primate animal model of parkinson disease by lateral ventricle administration of MPP<+> belongs to the biomedical technology. The method comprises the following steps: 1, preparing a lateral ventricle administration device; 2, according to a monkey brain atlas and nuclear magnetic resonance imaging data, acquiring a positioning coordinate of a lateral ventricle; 3, inserting a catheter into a lateral ventricle administration position through a monkey stereotaxic instrument, and beginning to administrate the drug after 5-8 days after the animal recovers; injecting a physiological saline solution containing MPP<+> into the lateral ventricle by an injector connected with the tail end of an administration pipe, with the MPP<+> concentration of 0.5-0.7 mg/mL and each administration dosage of 0.1-0.3 mL; carrying out administration one time every 48-72 hours, and after administration for 30-60 days, establishing the non-human primate animal model of the parkinson disease. The method employs a new administration mode that the MPP<+> (1-methyl-4-phenylpyridinium iodide) drug is directly injected into the lateral ventricle of the animal for modeling. With lateral ventricle injection, MPP<+> can perform whole brain circulation together with cerebrospinal fluid to specifically damage dopamine neurons in a brain and cause symptoms of the parkinson disease, and can furthest avoid toxic and side effects of the drug on a periphery.

The embodiment of the invention discloses a brain image classification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining target resting state function image information of a brain region of a target object, and enabling the target resting state function image information to be obtained based on a resting state function magnetic resonance imaging technology; the target resting state function image information is processed, a target brain network map of the target object is obtained, and the target brain network map comprises target brain function connection images of the target object at at least two preset time points; inputting each target brain function connection image corresponding to the target brain network atlas into a pre-trained brain atlas classification model to obtain a prediction probability that the target object belongs to each preset category, the brain atlas classification model is obtained based on training of the sample brain network atlas and classification labels corresponding to the sample target brain network atlas. According to the technical scheme of the embodiment of the invention, the classification of the brain image can be accurately predicted.

The invention discloses a method for constructing a brain map based on a differential geometry technology. The method comprises the following steps: for N volume data sets, sequentially taking any one yi as an initial template, and registering the yi to all N yjs based on a convolutional neural network framework to obtain each registration domain phi; calculating an average domain of each registration domain phi through an average transform domain to obtain N average domains avg; and enabling the average domain to act on yi to obtain a new template, so when the average domain of the N temporary templates meets a set target, N template images are obtained, and the N template images are used as fixed images during registration. According to the method, better performance can be achieved in construction of brain MRI atlas, and a standard template is provided for disease diagnosis of doctors.

The invention discloses a method for identifying a brain region position of fresh and live in-vitro brain tissue. The method comprises the following steps: S1. obtaining a brain tissue atlas identification model corresponding to an experimental object from a brain atlas model library according to the experimental object where the fresh and live in-vitro brain tissue is derived; S2. acquiring brainslice image signals of the fresh and live in-vitro brain tissue; S3. extracting an eigen value of the brain slice image signals from the brain slice image signals; and S4. analyzing the extracted eigen value by adopting the brain tissue atlas identification model, so as to identify a brain atlas corresponding to a brain slice image of the fresh and live in-vitro brain tissue and identify variousbrain regions on the brain slice image. The invention discloses a device for identifying the brain region position of the fresh and live in-vitro brain tissue. The device comprises an image obtainingmodule, an image acquisition module, an image extraction module and an image identification module. According to the method and the device, the transformation to that a brain region structure of an in-vitro fresh and live brain tissue slice is observed, identified and labeled by machines from that the brain region structure of the in-vitro fresh and live brain tissue slice is observed and identified manually can be achieved.

The embodiment of the invention provides a brain map construction and neural circuit detection method and a related product. The method comprises the following steps: acquiring first target data according to a first image and acquiring second target data according to a second image; determining a first brain map according to the first target data and the second target data, wherein the first brain map is used for representing a relationship between the first target data and the second target data; inputting the first brain map into a first neural network, and outputting a first feature which is used for representing a high-order topological feature of the first brain map; the first feature is input into a second neural network, a first neural circuit is output, the second neural network is used for decoupling the areas in the first brain atlas, the first neural circuit is used for representing the connection relation between the areas of the first brain atlas, and the purpose of detecting the neural circuit can be achieved.

The brain atlas creation apparatus includes: an image acquisition unit that acquires a brain image; a section setting unit that classifies a brain area in the brain image acquired by the image acquisition unit into a plurality of layers from a major category which is a higher layer to a minor category which is a lower layer and sets a brain section for each category; and a display control unit that performs control to switch the brain image for each category classified by the section setting unit and to display the brain image on a display unit.