44 results about "Corpus callosum" patented technology

Brain Magnetic Resonance Imaging (MRI), Computerized Tomotography (CT), or other diagnostic modalities may employ a three-step procedure during initial (“scout”) cranial pre-scans that corrects for patient positioning (i.e., roll, yaw and pitch) to reduce inter- and intra-patient variation, thereby enhancing the diagnostic and comparative value of subsequent detail scans even across different diagnostic platforms. In MRI, for instance, locating the saggital sinus (SS) and optimizing a line to bisect the brain through this SS may be automated to correct for roll and yaw. By then identifying the contour of the corpus callosum in a lateral saggital scout scan, the Talairach anterior commissure (AC)—posterior commissure (PC) reference line may be found for correcting pitch. Prescription of detailed scans are improved, especially when the three-step correction is repeated periodically identifying the need to repeat a detailed scan or to adjust the coordinates of a subsequent scan.

A method and system for automatic MR brain scan planning is disclosed. The method utilizes a set of 2D orthogonal localizer images to determine scanning planes for 3D diagnostic MR scans. A location of the mid-sagittal plane (MSP) is detected in each of a transversal localizer image and a coronal localizer image. A sagittal scanning plane is determined based on the location of the MSP in the transversal and coronal localizer images. A diagnostic sagittal MR scan is then acquired based on the sagittal scanning plane. The corpus callosum CC is segmented in a sagittal MR image slice resulting from the diagnostic sagittal MR scan. A transversal scanning plane can be determined based on a location of the CC in the sagittal MR image slice and the location of the MSP in the coronal localizer image, and a coronal scanning plane can be determined based on the location of the CC in the sagittal MR image slice and the location of the MSP in the transversal localizer image.

A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume.

A method and system for segmentation of the corpus callosum in MR brain images is disclosed. The method utilizes an active shape model (ASM) with confidence weighting to iteratively adjust an initial corpus callosum contour to define a boundary of the corpus callosum in an MR image. An ASM is used to determine a displacement value in a perpendicular direction of the corpus callosum contour for each node of the corpus callosum contour. The displacement value for each node is then weighted based on a confidence of that displacement value. The ASM is then fitted to the adjusted contour. These steps are iteratively performed until the contour converges to define the corpus callosum boundary. This boundary can be further refined based on intensity distributions in object and background regions defined by the boundary.

The invention discloses a method and system for automatically extracting the extracting magnetic resonance image corpus callosum. The method comprises the steps of according to a midsagittal plane T1 weighted nuclear magnetism brain image, determining a brain outline threshold value and a corpus callosum threshold value; performing threshold segmentation on the image according to the brain outline threshold value and the corpus callosum threshold value to respectively obtain a brain outline binary image and a corpus callosum binary image; respectively performing margin tracing on the brain outline binary image and the corpus callosum binary image to acquire a brain outline position and a corpus callosum margin cluster; establishing a decision tree, and screening marginal information to determine a corpus callosum region; determining an angle of inclination and a central position of a brain scanning line set according to the corpus callosum region. By means of the method and system, the corpus callosum in the midsagittal plane T1 weighted nuclear magnetism brain image can be extracted automatically, automatic regulation of the scanning line set during brain scanning can be completed according to information on the corpus callosum and the like, scanning efficiency is increased, no manual regulation is needed, time and labor are saved, an strong practicability is achieved.

The present invention is directed to a method of assessing in vivo human glial cell response to pathogenic infection that involves providing a non-human mammal either with at least 30% of its glial cells in its corpus callosum being human glial cells and / or with at least 5% of its glial cells its brain and brain stem white matter being human glial cells, subjecting the non-human mammal to pathogenic infection and assessing the in vivo human glial cell response to pathogenic infection. A method of identifying therapeutic agents for the pathogenic infection as well as forms of the non-human mammal having a pathogenic brain infection are also disclosed.

The invention discloses a construction method of a brain corpus callosum segmentation prediction image for corpus callosum state evaluation, which comprises the following steps: drawing a corpus callosum initial contour line from an acquired fetal brain ultrasonic image, and calculating a key point offset of the corpus callosum initial contour line; and cutting a brain corpus callosum segmentation prediction image from the fetal brain ultrasound image according to the initial contour line of the corpus callosum and the key point offset. According to the deep neural network model for fetal ultrasound image state analysis, the blank of brain ultrasound image brain corpus callosum state analysis is filled, and a method for evaluating the brain corpus callosum state through a brain ultrasound image is created for the first time.

The invention provides a method for measuring a fetal corpus callosum volume by using magnetic resonance imaging, and a magnetic resonance imaging apparatus. The method comprises the following steps:acquiring a positioning image of a to-be-detected fetus through magnetic resonance imaging; determining a detection area P according to the position of fetal corpus callosum in the positioning image,wherein the detection area P comprises the fetal corpus callosum; carrying out magnetic resonance scanning on the detection area to obtain a diffusion weighted image of the detection area, wherein a gradient direction during the magnetic resonance scanning only needs to be applied to a direction parallel to the extension direction of the fiber bundles of the corpus callosum; intercepting a fetal head image in the diffusion weighted image; applying a preset threshold to the fetal head image to obtain high signals with brightness greater than a threshold in the image; and carrying out bunching processing on the high signals, taking the maximum bunch in the high signals as corpus callosum, calculating the sum of the sizes of voxels related to the high signal of the maximum bunch, and taking the sum as the volume of the corpus callosum.

A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume.

The present invention is directed to a method of assessing in vivo human glial cell response to pathogenic infection that involves providing a non-human mammal either with at least 30% of its glial cells in its corpus callosum being human glial cells and / or with at least 5% of its glial cells its brain and brain stem white matter being human glial cells, subjecting the non-human mammal to pathogenic infection and assessing the in vivo human glial cell response to pathogenic infection. A method of identifying therapeutic agents for the pathogenic infection as well as forms of the non-human mammal having a pathogenic brain infection are also disclosed.

The present invention is directed to a non-human mammal with at least 30% of all of its glial cells in its corpus callosum being human glial cells and / or at least 5% of all of its glial cells in the white matter of its brain and / or brain stem being human glial cells. Methods of producing and using the non-human mammal are also disclosed.

The invention discloses an integrated positioning and segmenting method for corpus callosum and cerebellar earthworm. The method comprises the following steps: acquiring an initial corpus callosum search area of a human brain ultrasonic image; obtaining a corpus callosum average template and a cerebellar earthworm average template from the corpus callosum image and the cerebellar earthworm image;taking the corpus callosum average template as a search basis to carry out template size adaptive sliding window search to obtain a corpus callosum accurate search result; performing active contour model algorithm iteration based on the final search result of the corpus callosum, and segmenting the corpus callosum to obtain contour and position information of the corpus callosum; performing adaptive template size sliding window search by adopting a new similarity comparison algorithm so as to obtain a cerebellar earthworm fine search result; and iterating the initial contour to obtain an accurate contour of the cerebellar earthworm, and smoothing the accurate contour to finally obtain the accurate contour of the cerebellar earthworm.

The invention discloses a method for positioning and segmenting a cerebellar earthworm part in an ultrasonic image. The comprises the following steps of: obtaining a human brain ultrasonic image and corpus callosum size and position information in the ultrasonic image, reading a cerebellar earthworm part image, and generating a cerebellar earthworm part average template image through an adversarial neural network; according to the corpus callosum position information, an area with a small pixel mean value is selected as an initial search area of the cerebellar earthworm part, reducing the position information Loc of the cerebellar earthworm part relative to the corpus callosum direction; taking the average template size of the adaptive cerebellar earthworm as the size of the initial searchresult of the cerebellar earthworm; performing adaptive template size sliding window search by adopting a new similarity comparison algorithm so as to obtain a cerebellar earthworm fine search result; and carrying out outer contour graph fitting on the direction position information Loc and the morphological characteristics of the cerebellar earthworm part to obtain an initial contour of the cerebellar earthworm part, and carrying out smoothing processing on the accurate contour to finally obtain cerebellar earthworm part contour information.

The invention provides a deep brain tumor and white matter fiber bundle relation model and a preparation method thereof, and aims to obtain 3D high-resolution scanning images of deep brain tumors, adjacent white matter fiber bundles and brain parenchyma through an MR multi-modal imaging sequence. The obtained original image data are input into modeling post-processing software, and image fusion, segmentation and reconstruction are carried out on deep brain tumors, internal hemorrhage, peripheral edema, adjacent functional fiber bundles and brain parenchyma structures to obtain a deep brain tumor three-dimensional anatomical model; and the model can be imported into a color 3D printer to print an individualized precise medical 3D printing solid model for the brain tumor. The front-and-backand up-and-down spatial relationship between white matter fiber bundles such as brain tumor and adjacent key visual bundles, cortical spinal cord bundles and corpus callosum and brain parenchyma is accurately positioned. The occurrence probability of postoperative visual and dyskinesia is reduced, and the requirements of precise medical evaluation and operation are met. The model is used for preoperative planning and operation simulation, serves as a precise medical teaching training model, and improves the medical training teaching effect.

The invention relates to the technical field of natural pharmaceutical chemistry and medicine, in particular to application of isoflavonoids, namely tectorigenin and tectoridin, in belamcanda chinensis for treating multiple sclerosis diseases. The scheme adopted by the invention is as follows: a model experiment is conducted that 2% of cuprizone is used to induce demyelination of a mouse, and the experimental result shows that tectorigenin and tectoridin can significantly improve the pathological alteration and ultrastructural changes of demyelination at the callosum part of the mouse in the cuprizone-induced demyelination model; both of tectorigenin and tectoridin can significantly reverse the performance of a decreased myelin-associated protein expression level of the demyelinated mouse induced by cuprizone; both of tectorigenin and tectoridin can significantly alleviate the depression-like behaviors and the spatial learning and memory impairments of the mouse in the demyelination model, and show a remarkable protective function. Therefore, the two isoflavonoids namely tectorigenin and tectoridin can be used to prepare medicine for treating multiple sclerosis related diseases.

The invention discloses a plug seedling ejecting and picking apparatus capable of protecting pot bodies. The apparatus comprises a pressing plate, a base, a seedling picking shovel and pot protecting holders; the pressing plate comprises a handle, a crossbeam and an external frame; the base comprises a base plate as well as positioning pins and ejecting pins which are fixedly installed on the base plate; the pot protecting holders are placed at the bottom of a plug tray before seedling production, when the plug seedlings are ejected, pot bodies and roots of the plug seedlings can be protected, so that plug seedlings are vertical and seedlings can be rapidly ejected. The apparatus has simple structure and operation convenience, after the plug tray of the plug seedlings can be fixed to a correct position by the positioning pins, the pressing plate is placed over the plug seedling, the pressing plate external frame and an external edge of the plug tray are aligned, and at the same time damage on branches and leaves of the plug seedlings due to pressing can be avoided, the plug seedlings can be smoothly ejected by pressing the pressing plate, the ejected plug seedlings are kept vertical, so that the seedling picking shovel can be used for rapidly picking seedlings; seedling picking efficiency is greatly improved, pot soil and pot seedling roots of the plug seedlings can be protected, efficiency of transplanting work and survival rate of the plug seedlings are transplanting are improved, and soil environment is not polluted.

The invention discloses a segmentation method for a cerebellar earthworm part in an ultrasonic image, and the method comprises the following steps: S1, inputting an ultrasonic image of the cerebellarearthworm part and the size and position information of corpus callosum in the ultrasonic image, and carrying out the preprocessing of the ultrasonic image of the cerebellar earthworm part; S2, observing an ultrasonic image of the cerebellar earthworm part according to medical prior knowledge, and judging direction information of the cerebellar earthworm part in the image; S3, according to the direction information obtained in the step S2 and the morphological characteristics of the cerebellar earthworm part, conducting outer contour graph fitting on the cerebellar earthworm part, and obtaining the initial contour of the cerebellar earthworm part; S4, performing algorithm traumatic injury and smoothing processing on the initial contours of the cerebellar earthworms obtained in the S3 to form precise contours of the cerebellar earthworms; S5, carrying out smoothing processing on the precise contour; and finally, the purpose of completely, accurately and smoothly segmenting the cerebellar earthworm part is achieved.

The invention relates to a construction method of a corpus callosum cut-off animal model, and belongs to the field of animal model research. According to the construction method of the corpus callosum cut-off animal model, damage to local brain tissue is small, bleeding is little, the operation method is easy to master, and the constructed animal model of the corpus callosum cut-off animal model can be applied to research of epilepsy treatment and has wide application prospects.

Aplasia of the corpus callosum or aspermatogenesis is diagnosed by investigating existence or expression of the BT-IgSF gene with use of a primer for PCR or probe for hybridization comprising a DNA coding for a novel cell adhesion molecule (BT-IgSF) defined in the following (A) or (B) or a partial sequence thereof: (A) a protein comprising the amino acid sequence of the amino acid numbers 1 to 409 of SEQ ID NO: 2; (B) a protein comprising an amino acid sequence of the amino acid numbers 1 to 409 of SEQ ID NO: 2 including substitution, deletion, insertion or addition of one or several amino acids and having a function as a cell adhesion molecule.

The invention discloses a brain image processing method, electronic equipment and a computer readable storage medium. The brain image processing method comprises the following steps: acquiring a three-dimensional magnetic resonance brain image; registering the three-dimensional magnetic resonance brain image into a standard coordinate system and carrying out image segmentation to obtain a segmented image; performing image extraction on the segmented image according to a region label to obtain a target image; performing curve extraction on the target image to obtain a template curve; and performing curve registration on the template curve and the target curve to obtain a deformation mark. According to the brain image processing method provided by the invention, curve registration is performed on the template curve and the target curve, so that the deformation mark for effectively representing the deformation state of the corpus callosum is obtained.