765 results about "Brain tumor" patented technology

This invention includes pharmaceutical compositions, methods and kits for the treatment or diagnosis of a malignant tumors, including brain tumors, and diseases or disorders characterized by abnormal brain tissue.

The present invention provides methods, devices, and systems for in vivo treatment of cell proliferative disorders. The invention can be used to treat solid tumors, such as brain tumors. The methods rely on non-thermal irreversible electroporation (IRE) or supra-poration to cause cell death in treated tumors. In embodiments, the methods comprise the integration of ultra-short electric pulses, both temporally and spatially, to achieve the desired modality of cell death.

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.

The present invention provides methods, devices, and systems for in vivo treatment of cell proliferative disorders. The invention can be used to treat solid tumors, such as brain tumors. The methods rely on non-thermal irreversible electroporation (IRE) to cause cell death in treated tumors.

Provided herein are substituted pyrrolopyridine heterocycles and substituted pyrazolopyridine heterocycles, pharmaceutical compositions comprising said heterocycles and methods of using said heterocycles in the treatment of disease. The heterocycles disclosed herein function as kinase modulators and have utility in the treatment of diseases such as cancer, allergy, asthma, inflammation, obstructive airway disease, autoimmune diseases, metabolic disease, infection, CNS disease, brain tumor, obesity, asthma, hematological disorder, degenerative neural disease, cardiovascular disease, or disease associated with angiogenesis, neovascularization, or vasculogenesis.

A method for brain tumor segmentation in multi-parametric 3D magnetic resonance (MR) images, comprising: determining, for each voxel in the multi-parametric 3D MR image sequence, a probability that the voxel is part of brain tumor; extracting multi-scale structure information of the image; generating multi-scale tumor probability map based on initial tumor probability at voxel level and multi-scale structure information; determining salient tumor region based on multi-scale tumor probability map; obtaining robust initial tumor and non-tumor label based on tumor probability map at voxel level and salient tumor region; and generating a segmented brain tumor image using graph based label information propagation. The present invention is capable of achieving statistical reliable, spatially compact, and robust tumor label initialization, which is helpful to the accurate and reliable tumor segmentation. And the label information propagation framework could partially alleviate the performance degradation caused by image inconsistency between images to be segmented and training images.

To gain a better understanding of brain tumor angiogenesis, new techniques for isolating brain endothelial cells (ECs) and evaluating gene expression patterns were developed. When transcripts from brain ECs derived from normal and malignant colorectal tissues were compared with transcripts from non-endothelial cells, genes predominantly expressed in the endothelium were identified. Comparison between normal- and tumor-derived endothelium revealed genes that were specifically elevated in tumor-associated brain endothelium. These results confirm that neoplastic and normal endothelium in human brains are distinct at the molecular level, and have significant implications for the development of anti-angiogenic therapies in the future.

The invention discloses a brain tumor segmentation method based on a deep neural network and a multi-modal MRI image. The method includes steps: constructing the deep neural network, wherein the deep convolution neural network includes two three-layer convolution layers, a three-layer full connection, and a classification layer, an input layer corresponds to the multi-modal MRI image, and each node of an output layer corresponds to a tumor classification label; performing MRI image preprocessing; training a network model; and testing the model, performing normalization on a to-be-segmented tumor image sequence by employing image blocks of an MRI image sequence and mean values and standard deviations thereof in a training process, inputting the normalized image sequence to the deep neural network with the optimization network connection weight, obtaining node values of the classification layer, and obtaining the tumor classification of a to-be-segmented brain tumor image. According to the method, tumor abstract topological characteristic information in the multi-modal MRI image is mined and extracted by employing the deep neural network, and high segmentation accuracy and high segmentation precision can be guaranteed in brain tumor segmentation of the multi-modal MRI images.

The invention provides an MRI (Magnetic Resonance Imaging) brain tumor localization and intratumoral segmentation method based on a deep cascaded convolution network, which comprises the steps of building a deep cascaded convolution network segmentation model; performing model training and parameter optimization; and carrying out fast localization and intratumoral segmentation on a multi-modal MRIbrain tumor. According to the MRI brain tumor localization and intratumoral segmentation method provided by the invention based on the deep cascaded convolution network, a deep cascaded hybrid neuralnetwork formed by a full convolution neural network and a classified convolution neural network is constructed, the segmentation process is divided into a complete tumor region localization phase andan intratumoral sub-region localization phase, and hierarchical MRI brain tumor fast and accurate localization and intratumoral sub-region segmentation are realized. Firstly, the complete tumor region is localized from an MRI image by adopting a full convolution network method, and then the complete tumor is further divided into an edema region, a non-enhanced tumor region, an enhanced tumor region and a necrosis region by adopting an image classification method, and accurate localization for the multi-modal MRI brain tumor and fast and accurate segmentation for the intratumoral sub-regions are realized.

The invention discloses a brain tumor segmentation network and segmentation method based on a U-Net network. The tail of a contraction path of the segmentation network is connected with a spatial pyramid pooling structure; hole convolution of different scales is introduced into a network jump connection part of the segmentation network; an Add operation and original input are adopted to form a residual block with hole convolution; a receptive field of shallow feature information in the contraction path is expanded; fusing with an expansion path of a corresponding stage is carried out. The segmentation method comprises the following steps: cutting and preprocessing a training data set, then constructing a brain tumor segmentation network DCU-Net based on a U-Net network, then inputting a preprocessed two-dimensional image into a segmentation model for feature learning and optimization, obtaining an optimal parameter model of the segmentation model, and finally inputting a to-be-segmented test data set image into the segmentation model for tumor region segmentation. According to the method, the problems of over-segmentation and under-segmentation in brain tumor segmentation can be effectively solved, and the brain tumor segmentation precision is improved.

The invention relates to a three-dimensional brain tumor image segmentation method based on an improved U-Net neural network. The method comprises the following steps: firstly, performing bias field effect removal processing of an N4ITK algorithm on three-dimensional brain tumor MRI image data, and secondly, respectively performing gray normalization preprocessing on each modal image in an original MRI image; building and training an improved U-Net convolutional neural network model; in a training process, inputting the four types of modal data of the patient as four channels of a neural network into an improved U-Net convolutional neural network model for training; the convolutional neural network U-Net being used as a basis, establishing an improved U-Net convolutional neural network model, and the improved U-Net convolutional neural network model comprising an analysis path used for extracting features and a synthesis path used for recovering a target object.

The application is directed toward a method of treating a brain tumor in a patient comprising systemically administering a monoclonal antibody.

The therapy with cannabinois in the treatment of cerebral tumors involves (intracranial or systematic) administration of (natural of synthetic) cannabinoids to (human or non-human) mammals having cerebral tumors. Activation of the specific receptors of the cannabinoids leads to selective death of the transformed cells. Regression or eradication of the cerebral tumors is achieved without any significant side-effects.

A method, using high intensity ultrasound, which may also be combined with a chemotherapy agent, that can result the direct destruction of tumor cells and in the reduction or elimination of local reoccurrence of cancer after removal of cancerous tissue, such as a surgical breast lumpectomy or surgical excision of a brain tumor. The method comprises either (1) the treatment of the tumor directly with High Intensity Focused Ultrasound, or (2) the treatment of the margins of the tissue surrounding the surgical cavity or void with ablative continuous wave high intensity ultrasound and a combination of a locally delivered chemotherapy agent and high intensity ultrasound, termed sonoporation. The present invention permits both the direct destruction (ablation) of tumor tissue as well as the destruction of tissue around the surgical margin and may include the enhanced local cellular uptake of locally injected chemotherapeutic drugs, all of which can be accomplished by a therapeutic ultrasound device used during surgery.

The present invention relates to the use of proteins that are differentially expressed in primary brain tumor tissues, as compared to normal brain tissues, as biomolecular targets for brain tumor treatment therapies. Specifically, the present invention relates to the use of therapeutic and imaging agents, which specifically bind to one or more of the identified brain tumor protein targets. The present invention also provides compounds and pharmaceutically acceptable compositions for administration in the methods of the invention. Nucleic acid probes specific for the spliced mRNA encoding these variants and affinity reagents specific for the novel proteins are also provided.

P5P can be used as effective treatments for the modulation of P2X7, IL-1β, and inflammation response, and for diseases in which prevention of P2X7-dependent pathways or prevention of release of IL-1β is desirable, such as epithelial cancer, leukemia, brain tumors, spinal cord injury, tuberculosis, Alzheimer's Disease, neurodegenerative diseases, autosomal recessive polycystic kidney disease, diabetes, including type I diabetes, prostate cancer, and osteoporosis, bone formation and resorption.

A method and system for brain tumor segmentation in multi-spectral 3D MRI images is disclosed. A trained probabilistic boosting tree (PBT) classifier is used to determine, for each voxel in a multi-spectral 3D MR image sequence, a probability that the voxel is part of a brain tumor. The brain tumor is then segmented in the multi-spectral 3D MRI image sequence using graph cuts segmentation based on the probabilities determined using the trained PBT classifier and intensities of the voxels in the multi-spectral 3D MR image sequence.

This invention relates to novel compounds which are inhibitors of the Rearranged during Transfection (RET) kinase, to pharmaceutical compositions containing them, to processes for their preparation, and to their use in therapy, alone or in combination, for the normalization of gastrointestinal sensitivity, motility and/or secretion and/or abdominal disorders or diseases and/or treatment related to diseases related to RET dysfunction or where modulation of RET activity may have therapeutic benefit including but not limited to all classifications of irritable bowel syndrome (IBS) including diarrhea-predominant, constipation-predominant or alternating stool pattern, functional bloating, functional constipation, functional diarrhea, unspecified functional bowel disorder, functional abdominal pain syndrome, chronic idiopathic constipation, functional esophageal disorders, functional gastroduodenal disorders, functional anorectal pain, inflammatory bowel disease, proliferative diseases such as non-small cell lung cancer, hepatocellular carcinoma, colorectal cancer, medullary thyroid cancer, follicular thyroid cancer, anaplastic thyroid cancer, papillary thyroid cancer, brain tumors, peritoneal cavity cancer, solid tumors, other lung cancer, head and neck cancer, gliomas, neuroblastomas, Von Hippel-Lindau Syndrome and kidney tumors, breast cancer, fallopian tube cancer, ovarian cancer, transitional cell cancer, prostate cancer, cancer of the esophagus and gastroesophageal junction, biliary cancer, adenocarcinoma, and any malignancy with increased RET kinase activity.

The invention relates to the medical apparatus field, aims to propose an improved full convolutional neural network to realize machine segmentation of a brain tumor nuclear magnetic resonance image, avoids low efficient and instability existing in manual segmentation, provides the rapid and reliable brain tumor segmentation result and provides accurate bases for diagnosis, treatment and operationof brain tumors. The method comprises steps that 1), an image is selected; 2), a full convolutional neural network FCN model is constructed; 3), the segmentation result is tested, when the FCN model is trained, the trained model is utilized to predict the tumor position and the boundary size of any brain tumor image, corresponding evaluation indexes are utilized to evaluate the segmentation result, and the FCN model can be better ameliorated. The method is advantaged in that the method is mainly applied to NMR image processing occasions.

Methods for treating brain tumors involving the administration of a compound that selectively inhibits pathological production of human VEGF are described. The compound can be administered as a single agent therapy or in combination with one or more additional therapies to a human in need of such treatment.

The present invention belongs to the computer-assisted medical field, and especially relates to a brain tumor automatic segmentation method through fusion of a full convolutional neural network and a conditional random field. The objective of the invention is to solve the problem that the depth learning technology cannot ensure the continuity of a segmentation result on the appearance and the space when the brain tumor segmentation is performed in the prior art. In order to solve the problem mentioned above, the method comprises the following steps: the step 1, employing a non-uniform offset correction and luminance regularization method to process the magnetic resonance image of the brain tumor image to generate a second magnetic resonance image; and the step 2, employing the neural network fusing the full convolutional neural network and the conditional random field to perform brain tumor segmentation of the second magnetic resonance image and output the brain tumor segmentation result. The brain tumor automatic segmentation method through fusion of the full convolutional neural network and the conditional random field can perform end-to-end brain tumor segmentation slice to slice and has higher operation efficiency.