125 results about "Multi organ" patented technology

A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.

Methods for the treatment of acute renal failure, multi-organ failure, early dysfunction of kidney transplant, chronic renal failure, organ dysfunction, and wound healing are provided. The methods include delivering a therapeutic amount of hematopoietic stem cells, non-hematopoietic, mesenchymal stem cells, hemangioblasts, and pre-differentiated cells to a patient in need thereof.

Fluidic multiwell bioreactors are provided as a microphysiological platform for in vitro investigation of multi-organ crosstalks for an extended period of time of at least weeks and months. The disclosed platform is featured with one or more improvements over existing bioreactors, including on-board pumping for pneumatically driven fluid flow, a redesigned spillway for self-leveling from source to sink, a non-contact built-in fluid level sensing device, precise control on fluid flow profile and partitioning, and facile reconfigurations such as daisy chaining and multilayer stacking. The platform supports the culture of multiple organs in a microphysiological, interacted systems, suitable for a wide range of biomedical applications including systemic toxicity studies and physiology-based pharmacokinetic and pharmacodynamic predictions. A process to fabricate the disclosed bioreactors is also provided.

A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.

The invention discloses an abdomen CT (Computed Tomography) image multi-organ segmentation method based on a superpixel. The method comprises the following steps that: preprocessing an abdomen CT image, wherein the preprocessing comprises the filtering and the gray scale mapping of the abdomen CT image; utilizing correlation between an upper layer and a lower layer in an image in an abdomen CT image set; in addition, using a K-means clustering method which takes fused Euclidean distance and gray scale distance as a measurement distance to segment the abdomen CT image into superpixel blocks; after the superpixel blocks are subjected to feature extraction, adopting an extreme learning machine to be combined with a position and gray scale statistical probability model to classify the superpixel blocks; and then, combining classified superpixel blocks, and obtaining an abdomen CT image multi-organ segmentation result. By use of the method provided by the invention, the abdomen CT image canbe accurately subjected to multi-organ segmentation, a calculated amount is lowered to a pixel block level from a pixel level, the calculated amount is greatly lowered, and abdomen CT image multi-organ segmentation speed is quickened.

A polymeric chip having at least one three-dimensional porous scaffold, a microfluidic channel inlet to the porous scaffold, and a microfluidic channel outlet from the porous scaffold. In one embodiment, the polymeric chip has two three-dimensional porous scaffolds: one scaffold comprises liver cells and the other scaffold comprises cancer cells. The chip can be used as a multi-organ tissue model system.

The present invention provides compounds of formula (I) or (Ia) which are ligands binding to the HDM2 protein, inducing apoptosis and inhibiting proliferation, and having therapeutic utility in cancer therapy and prevention.Compounds of formula (I) or (Ia) can be used as therapeutics for treating stroke, myocardial infarction, ischemia, multi-organ failure, spinal cord injury, Alzheimer's Disease, injury from ischemic events and heart valvular degenerative disease.Moreover, compounds of formula (I) or (Ia) can be used to decrease the side effects from cytotoxic cancer agents, radiation and to treat viral infections.

The invention discloses an abdominal multi-organ nuclear magnetic resonance image segmentation method and system based on FCN, and a medium. The abdominal multi-organ nuclear magnetic resonance imagesegmentation method comprises the following implementation steps: acquiring an input image and carrying out data preprocessing and image normalization operation; inputting the normalized abdominal multi-organ nuclear magnetic resonance image into a trained high-resolution full convolutional neural network model to obtain a final prediction image, wherein the high-resolution full convolutional neural network model is pre-trained to establish a mapping relationship between the normalized abdominal multi-organ nuclear magnetic resonance image and the corresponding final prediction image; and activating the final prediction graph by using an activation function to obtain a prediction score graph, and taking a category with the highest prediction score at each pixel position as a prediction label category of the pixel position to obtain a final segmentation prediction graph. According to the abdominal multi-organ nuclear magnetic resonance image segmentation method, automatic segmentation of the abdominal multi-organ nuclear magnetic resonance image can be realized, for example, the abdominal multi-organ MR image is segmented according to five different region types of an organ-free region, a liver region, a right kidney region, a left kidney region and a spleen region.

The invention discloses a contour perception multi-organ segmentation network construction method based on class-by-class convolution operation. The contour perception multi-organ segmentation networkconstruction method comprises the following steps: 1, performing region coarse segmentation and edge detection on multiple organs of the abdomen; 2, introducing a semantic-guided class-by-class multi-scale attention mechanism; step 3, performing class-by-class fusion of multi-branch information; step 4, performing introduction of multi-task loss. According to the invention, the advantages of a convolutional neural network and a gated recurrent neural unit are utilized, and for the characteristics and difficulties of a multi-organ segmentation task, via the contour information assisted multi-scale feature extraction, a class-by-class multi-scale semantic attention mechanism, a class-by-class cavity convolution fusion mechanism and a plurality of loss functions can be introduced to relievethe inter-class imbalance problem of organs; multi-organ segmentation is performed on a three-dimensional CT image more efficiently and accurately, and the advantages of the invention are verified ona data set containing 14 types of organ labels; the invention can be widely applied to computer-aided diagnosis and treatment application, such as endoscopic surgery, interventional therapy and radiotherapy plan making.

An orally-taken Chinese medicine in the form of powder or others for treating the calculosis of multiple organs is prepared from 24-25 Chinese-medicinal materials including scutellaria root, phellodendron bark, capejasmine fruit, tuckahoe, etc. Its preparing process is also disclosed.

The invention provides a multi-organ chip based on microfluidic technology and a preparation method thereof. The microfluidic chip is mainly composed of an upper chip, a carbonate film and a lower chip. The preparation method is as follows: the upper chip and the carbonate film are sealed with oxygen plasma; after the upper surface of the upper and lower chips is dipped in PDMS, it is aligned and bonded with the upper chip that has been irreversibly sealed with the carbonate film. The advantages of the present invention are: using the multi-layer microfluidic chip integration method, combining the culture units of various cells, and assembling and constructing the models of various organs on the same chip according to the sequence of operation between organs in the body , realizes the multi-organ combination chip construction method conveniently and quickly, and the organs can be effectively divided, which is convenient for the separate control and observation of each organ.

Monoclonal IgM antibodies which promote central nervous system remyelination when given to a mammal afflicted with a demyelinating disease are disclosed. These antibodies show multi-organ autoreactivity, and recognize both surface and cytoplasmic determinants on glial cells.

An application of the Chinese medicine prepared from red sage root, notoginseng and borneol in preparing the medicines for treating multi-organ injury caused by metabolic syndrome is disclosed.

The inventors have unexpectedly discovered that shock and / or potential multi-organ failure due to shock can be effectively treated by administration of liquid high-dose protease inhibitor formulations to a location upstream of where pancreatic proteases are introduced into the gastrointestinal tract. Most preferably, administration is directly to the stomach, for example, via nasogastric tube under a protocol effective to treat shock by such administration without the need of providing significant quantities of the protease inhibitor to the jejunum and / or ileum.

The invention discloses a thoracic cavity multi-organ segmentation method based on a cascade residual full convolutional network. The method comprises the following steps: roughly segmenting each organ by using a rough segmentation model for positioning the range of each organ, then using the single fine segmentation model of each organ for carrying out fine segmentation on the coarsely segmentedand positioned region to obtain a fine segmentation result of each organ, finally, combining the results to obtain a final multi-organ segmentation result, and reserving more details in the segmentation result. The method is improved on the basis of u-net, and residual connection and an attention mechanism of a feature dimension are introduced into a down-sampling module in u-net, so that the network is easier to train, and has an automatic feature selection capability. Besides, a cascading strategy is introduced, and a staged segmentation network is used, so that rapid and accurate segmentation of multiple organs of the thoracic cavity CT is realized.

Methods and a composition for the treatment of organ dysfunction, acute renal failure, multi-organ failure, early dysfunction of kidney transplant, graft rejection, chronic renal failure, wounds, and inflammatory disorders including media conditioned by mesenchymal stem cells are provided. Methods for modulation of growth factor and cytokine expression including administering a therapeutic amount of mesenchymal stem cells, endothelial cells derived from mesenchymal stem cells, or media conditioned by mesenchymal stem cells are also provided.

The embodiment of the invention provides a CT image abdominal multi-organ segmentation method and device. The method comprises the steps of obtaining a first image sequence and a second image sequenceaccording to an abdominal CT image sequence to be segmented; inputting the first image sequence and the second image sequence into a multi-organ segmentation model, and outputting a multi-organ segmentation result of each abdominal CT image in the abdominal CT image sequence to be segmented; wherein the scale of the image in the first image sequence is greater than that of the image in the secondimage sequence; wherein the multi-organ segmentation model is obtained after training based on abdominal CT image sequence sample data and a predetermined manual segmentation marking result. According to the CT image abdominal multi-organ segmentation method and device provided by the embodiment of the invention, the multi-organ segmentation result of each abdominal CT image in the abdominal CT image sequence to be segmented is obtained according to the context information of the organ levels obtained by the two different scales, and a more accurate and stable segmentation result can be obtained.

The invention discloses an image detection method, a training method of a related model, a related device and equipment, and the method comprises the steps: obtaining a sample medical image, wherein an actual region of at least one unlabeled organ of the sample medical image is pseudo labeled; detecting the sample medical image by using the original detection model to obtain a first detection result of a first prediction region comprising the unlabeled organ; detecting the sample medical image by using an image detection model to obtain a second detection result including a second prediction area of the unlabeled organ, wherein the network parameters of the image detection model are determined based on the network parameters of the original detection model; and adjusting the network parameters of the original detection model by using the difference between the first prediction area and the actual area and the difference between the first prediction area and the second prediction area.According to the scheme, the detection accuracy can be improved during multi-organ detection.

Fluidic multiwell bioreactors are provided as a microphysiological platform for in vitro investigation of multi-organ crosstalks with microbiome for an extended period of time of at least weeks and months. The platform has one or more improvements over existing bioreactors, including on-board pumping for pneumatically driven fluid flow, a redesigned spillway for self-leveling from source to sink, a non-contact built-in fluid level sensing device, precise control on fluid flow profile and partitioning, and facile reconfigurations such as daisy chaining and multilayer stacking. The platform supports the culture of multiple organs together with microbiome in a microphysiological, interacted systems, suitable for a wide range of biomedical applications including systemic toxicity studies and physiology-based pharmacokinetic and pharmacodynamic predictions. A process to fabricate the bioreactors is also provided.

The present invention provides a compound selected from compounds of formula I as ligand binding to the HDM2 protein, inducing apoptosis and inhibiting proliferation, and having therapeutic utility in cancer therapy. Compounds of formula (I) can be used as therapeutics for treating stroke, myocardial infarction, ischemia, multi-organ failure, spinal cord injury, Alzheimer's Disease, injury from ischemic events, heart valvular degenerative disease Moreover, compounds of formula (I) can be used to decrease the side effects from cytotoxic cancer agents and to treat viral infections.

A discovery process beginning with an in vivo screening of proteins, peptides, natural products, classical medicinal compound or other substances. The administration of compounds to the animal can be either direct or indirect, such as by the administration and expression of cDNA-containing plasmids. Since the discovery process of the invention is based on a non-preconceived hypothesis and whole organism multi-organ analysis, a compound can be selected for testing in the absence of any biological selection criteria. The resulting organism-wide pattern of the gene expression changes in the transcriptome provides an overview of the activities at the molecular and organism-wide levels. The discovery process of the invention then integrates in vivo profiling and internal and external genomic databases to elucidate the function of unknown proteins, typically within few months. The invention further relates to medical uses of fibroblast growth factor 23 (FGF-23), FGF-23 fragments, FGF-23 C-terminal polypeptides, FGF-23 homologs and / or FGF-23 variants.

The invention discloses a multi-organ segmentation method based on self-supervised feature small sample learning, and mainly solves the problem of poor multi-organ segmentation effect by using a small sample learning segmentation method in the prior art. According to the scheme, the method comprises the steps of: using a superpixel segmentation method for generating a large amount of data containing pseudo labels from an initial data set, and selecting images and the pseudo labels from the data to serve as a support set; generating a query set by adopting a data enhancement method; extracting image features of the support set and the query set through a pre-trained encoder by using self-supervised feature learning, and then calculating the similarity of the image features to obtain foreground information and prior probability auxiliary information feature maps; constructing a segmentation network to carry out feature refining on the foreground information to obtain a support set prototype; and calculating a classification probability according to the support set prototype and the prior probability auxiliary information feature map to obtain a segmentation result. The method reduces over-segmentation and under-segmentation phenomena of large target organs, improves recognition of small target organs, can be used for multi-organ segmentation of medical images, and assists doctors in diagnosing diseases.

Compositions for the treatment of shock, autodigestion, multi-organ failure, intestinal ischemia, or intestinal hypoperfusion are provided.