42 results about "Hippocampus proper" patented technology

Methods for measuring atrophy in a brain region occupied by the hippocampus and entorhinal cortex. In one example, MRI scans and a computational formula are used to measure the medial-temporal lobe region of the brain over a time interval. This region contains the hippocampus and the entorhinal cortex, structures allied with learning and memory. Each year this region of the brain shrank in people who developed memory problems up to six years after their first MRI scan. The method is also applicable for measuring the progression rate of atrophy in the region in an instance where the onset of Alzheimer's disease has already been established.

The invention discloses a hippocampus extraction method of a human brain nuclear magnetic resonance image based on a 3D neural network. The method comprises the following steps: preprocessing an original image data set and a label, constructing a 3D hippocampus segmentation neural network model, defining a boundary enhancement loss function, optimizing the boundary enhancement loss function, and detecting a preprocessed to-be-detected image by using the trained 3D hippocampus segmentation neural network model to obtain a hippocampus extraction result. According to the method, the 3D hippocampus segmentation neural network model is utilized, efficient, automatic and accurate segmentation of the hippocampus structure in human brain MRI is achieved, and the time for a doctor to perform earlydiagnosis on Alzheimer's disease can be shortened.

The invention discloses a method for inducing body color change of young hippocampuserectus. In the method, the 20-day-old young hippocampuserectus is induced under physical environmental factors, so that the body color of the young hippocampuserectus is changed from black to yellow or saffron yellow, the color can be kept for a long term, and the survival rate of the young hippocampuserectus canalso be remarkably improved. The body color of the young hippocampuserectus is subjected to induced change by using the method disclosed by the invention, thus the market value of the living hippocampuserectus can be increased, and the commercial culture efficiency of the hippocampuserectus is favorably improved.

The embodiment of the invention provides a hippocampal body segmentation method, a device and an electronic device applied to MRI. The method comprises the following steps: acquiring MRI three-dimensional images; constructing a target image having a preset size and containing a sensitive region of the MRI three-dimensional image; inputting the target image into at least two interactive neural networks trained in advance respectively, and obtaining the pre-segmentation results output by each interactive neural network; determining a segmentation result of the MRI three-dimensional image according to a predetermined weight of each of the interactive neural networks and a pre-segmentation result output by each of the interactive neural networks. The embodiment of the invention can improve theefficiency of segmenting the hippocampus.

The invention relates to a medical image processing device and method using a convolutional neural network. A data enhancement technology and a migration technology are utilized; supervised learning of a convolutional neural network for deep learning of artificial intelligence is achieved by using one to twenty two-dimensional slices passing through a coronal plane of a hippocampus of image data of nuclear magnetic resonance imaging of a three-dimensional skull of a subject; one or two two-dimensional images passing through the coronal plane of the hippocampus of the three-dimensional skull nuclear magnetic resonance imaging image data of the subject is selected as tested images so as to realize three-class distinguishing and identification of the normal senile group, the forgetting type mild cognitive impairment and the Alzheimer's disease.

The invention discloses a hippocampus three-dimensional semantic network segmentation method based on a multi-scale feature multi-path attention fusion mechanism, and belongs to the field of medical image processing. The method comprises the following steps: preprocessing public marked hippocampus image data, and cutting original data into image blocks containing a hippocampus; a brand new hippocampus three-dimensional semantic segmentation network structure is constructed based on multi-scale feature extraction, a multi-path attention fusion mechanism and a branch classifier ensemble learning strategy; dividing the data set; performing offline model training to obtain model weight parameters for the three-dimensional hippocampus structure; and segmenting the test set image by using the model file and evaluating a segmentation result. According to the method, the semantic segmentation network structure conforming to the characteristics of the three-dimensional hippocampus image is designed, so that the utilization rate of the network on multi-dimensional image information can be improved, the pixel intensive prediction capability is improved, and the hippocampus segmentation performance is improved.

The invention relates to the technical field of neural electrodes, in particular to a flexible neural electrode, a preparation method and equipment. The flexible neural electrode comprises a flexible implantation part and a rear end interface part, the flexible implantation part comprises at least one flexible probe, one end of the flexible probe is connected with the rear end interface part, and the flexible probe is used for being implanted into a deep brain area of a living body; the flexible probe comprises a metal electrode and a flexible supporting layer, and the metal electrode is arranged in the flexible supporting layer; electrode holes are formed in the flexible supporting layer, and the metal electrodes are exposed out of the flexible supporting layer through the electrode holes to form electrode points; and the flexible supporting layer is made of polyimide. According to the flexible neural electrode, the implantation depth and the transverse coverage area are increased on the device structure, polyimide is adopted as the flexible supporting layer, and the electrode structure is manufactured through the MEMS technology. The flexible neural electrode has good biocompatibility, and can be applied to clinical research aiming at use scenes of being implanted into deep brain areas such as hippocampus and the like.

A hippocampal prosthesis for bypassing a damaged portion of a subject's hippocampus and restoring the subject's ability to form long-term memories. The hippocampal prosthesis includes a first set of hippocampal electrodes configured to receive an input signal from at least one of the subject's hippocampus or surrounding cortical region. The hippocampal prosthesis includes a processing device having a memory and one or more processors operatively coupled to the memory and to the first set of hippocampal electrodes. The processing device being configured to generate an output signal based on the input signal received from the first set of hippocampal electrodes. The hippocampal prosthesis includes a second set of hippocampal electrodes operatively coupled to the one or more processors and configured to receive and transmit the output signal to the subject's hippocampus.

The invention discloses a robot behavior learning system based on a striated body structure and a learning method thereof, and belongs to the technical field of bionics. The robot behavior learning system is composed of sensory cortex, motor cortex, hippocampus, thalamus, a black compact part, an abdominal side quilt area and a striated body, wherein the striated body comprises striated corpusclesand a matrix. The striped corpuscles receive positioning information generated by hippocampus position cells and dopamine information generated by a black compact part and an abdominal covered area,and update orientation information of the robot according to an operation conditioned reflex mechanism. The matrix receives orientation information of the striped corpuscles and performs action selection according to an improved epsilon-greedy algorithm. Behavior habits can be formed after the robot interacts with the environment for a period of time. According to the method, a possible explanation of animal habitual behavior generation is given, and robot autonomous behavior learning can be guided. The method can be applied to the fields of robot navigation, physiology, animal behavioristicsand the like.

The invention provides an MRI (Magnetic Resonance Imaging) hippocampus segmentation method and system based on a hypergraph numerical neural P system. The method comprises the following steps: acquiring an MR image to be segmented; inputting the MR image to be segmented into the hypergraph-based numerical neural P system to obtain a hippocampus segmentation result; wherein the supergraph-based numerical neural membrane system comprises superneurons, and an encoder and a decoder consisting of a plurality of basic neurons, the encoder and the decoder are used for learning up-sampling and down-sampling features of the hippocampus in the MR image, and the superneurons are used for jumping and series fusion between the encoder and the decoder. The high precision of the deep learning model in semantic segmentation is combined with the good parallelism and robustness of the membrane system, and the performance of hippocampus segmentation is improved.

The invention discloses application of a deacetylase inhibitor in preparation of a medicine for treating abnormal glucose metabolism, and relates to the field of abnormal glucose metabolism treatment. The invention provides application of a deacetylase inhibitor in preparation of a medicine for treating abnormal glucose metabolism. The invention provides a new application way of the deacetylase inhibitor, the deacetylase inhibitor can regulate the expression of differential proteins generated by abnormal glucose metabolism, and a new drug choice is provided for the treatment of abnormal glucose metabolism diseases, especially for abnormal glucose metabolism of hippocampus tissues caused by fluorine poisoning.