518 results about "Medical imaging data" patented technology

A system and methods for more efficient review, processing, analysis and diagnoses of medical imaging data is disclosed. The system and methods include automatically segmenting and labeling imaging data by anatomical feature or structure. Additional tools that can improve the efficiency of health care providers are also disclosed.

To provide simple yet accurate stent graft fenestration, a patient-specific fenestration template is used as a guide for graft fenestration. To generate the fenestration template, a patient's medical imaging data such as CT scan data may be used to generate a 3-D digital model of an aorta lumen of the patient. The aorta lumen may encompass one or more branch vessels, which may be indicated on the 3-D digital model. Based on the 3-D digital model or a segment thereof, the fenestration template may be generated, for example, using 3-D printing technology. The fenestration template may include one or more holes or openings that correspond to the one or more branch vessels. To fenestrate a stent graft, the fenestration template is coupled to the stent graft so that the holes or openings on the fenestration template indicate the fenestration locations.

The invention relates to a materialized heart 3D model manufacturing method capable of achieving internal and external structures. The method can overcome the defects in the prior art. The method includes the steps that (1) the heart part of a patient is scanned to obtain medical image data to form a DICOM file; (2) the DICOM file obtained in the step (1) is identified through Mimics software and stored to form a computer-identified .mcs file; (3) different data templates are extracted from the software; (4) the templates, with cavity structures or incomplete images or unclear boundaries, obtained in the step (3) are processed to be made clear and complete; (5) a needed template is formed by adding or deleting or separating or combining the templates; (6) a 3D image formed in the step (5) is processed to enable the exterior of the 3D image to be smooth and interior image templates to be complete to form an STL file; (7) data processed in the step (6) are input in a 3D laser printer, and accordingly a heart model is printed. According to the method, heart models with clear and complete structures can be obtained, and the success rate of heart operations can be greatly increased.

A method of managing a clinical trial includes verifying compliance with pre-determined data collection protocols in real time of medical data collected by peripheral clinical trial centers participating in the clinical trial, sending compliance verifications, collecting review reports submitted by the reviewers on the medical data, such as medical imaging data, according to a predetermined question-and-answer format, evaluating the answers in the review reports, for concordance, and transmitting at least one electronic notification pertaining to a result obtained from the evaluating, wherein at least one of the verifying, sending, collecting, evaluating, and transmitting is performed using at least one processor. A system for implementing the method also is provided.

The invention relates to the field of auxiliary medical diagnosis and aims to provide an auxiliary diagnosis system for interpreting medical image features based on the deep learning method. The system comprises the following steps of: reading medical image data of a lesion and preprocessing the medical image data; selecting an image, establishing a convolutional neural network architecture, automatically learning and segmenting a lesion area, and refining the shape of the lesion; constructing a CNN model of a convolutional neural network architecture to automatically interpret the characteristics of benign and malignant lesions, and acquiring an auxiliary diagnosis system for interpreting medical image features based on the depth learning method after training. The invention not only canautomatically divide the focus area by means of the depth convolution neural network, make up the deficiency that the weak boundary problem cannot be solved based on the active contour and the like, but also can automatically learn the characteristic combination extracted with the value, thereby avoiding the complex of manually selecting the feature.

A method and apparatus for deep learning based fine-grained body part recognition in medical imaging data is disclosed. A paired convolutional neural network (P-CNN) for slice ordering is trained based on unlabeled training medical image volumes. A convolutional neural network (CNN) for fine-grained body part recognition is trained by fine-tuning learned weights of the trained P-CNN for slice ordering. The CNN for fine-grained body part recognition is trained to calculate, for an input transversal slice of a medical imaging volume, a normalized height score indicating a normalized height of the input transversal slice in the human body.

The embodiment of the invention discloses a medical diagnosis and treatment system. The system comprises a diagnosis and treatment data integration module used for obtaining diagnosis and treatment data related to a patient from one or more medical information sources and integrating the diagnosis and treatment data, wherein the diagnosis and treatment data at least comprises medical image data and medical text data; an artificial intelligence image analysis module used for analyzing the medical image data based on an artificial intelligence image analysis technology to generate an image analysis report; and an artificial intelligence text analysis module used for performing information extraction on the medical text data and/or the image analysis report based on an artificial intelligenceinformation extraction technology to obtain structured diagnosis and treatment data related to diseases.

A system and method is provided for performing and monitoring a magnetic nanoparticle hyperthermia process on a subject having received a dose of a magnetic nanoparticle configured to bind to tissue within a target area of the subject. A low-field MRI system is utilized having a static magnetic field and a radio frequency (RF) system configured to receive MRI data from the target area. A hyperthermia system is coupled to the low-field MRI system and configured to generate a hyperthermia excitation field configured to cause the magnetic nanoparticle to rotate at a lag with respect to a magnetic field experienced by the magnetic nanoparticle to cause a temperature increase in the target area. The MRI system is configured to acquire medical imaging data from the target area and reconstruct images therefrom indicating at least one of a spatial distribution of the nanoparticle in and a temperature of the target area.

A system for admitting medical imaging data comprising image data and associated metadata comprises input means arranged to received image data from at least one source, a memory having stored therein consistency data defining at least one consistency criterion, and processing means arranged to analyse the imaging data to determined whether it meets the consistency criterion, and if it does not to amend the imaging data so that it does.

In a method and apparatus for generating an enhanced image for display from medical imaging data of a subject, a feature of interest in the imaging data elongated in at least one dimension is determined. A location of a line through the imaging data along the feature of interest is obtained, and a projection image along the line is generated from the imaging data.

A method of resampling medical imaging data from a first spatial distribution of data points onto a second spatial distribution of data points, including determining a matrix of reverse interpolation coefficients for resampling data from said second spatial distribution onto said first spatial distribution, inverting a matrix based on said reverse interpolation matrix to determine forward resampling coefficients for resampling data from said first spatial distribution to said second spatial distribution, and resampling data from said first spatial distribution onto said second spatial distribution using said forward resampling coefficients.

An encephalic electrode individualization locating method based on multimode medical image data fusion relates to the fields of medical instruments and medical image processing and neuroimaging. By means of multimode medical image data including magnetic resonance images, X-ray images and intranperative optical photos, the space position relation of an encephalic electrode and a brain tissue structure is comprehensively measured, a relation between the position of the encephalic electrode and a brain anatomical structure is established, and accurate and fast individualization locating of the encephalic electrode is achieved. The encephalic electrode individualization locating method based on multimode medical image data fusion can be used in the fields of cognitive neuroscience basic research, clinic neurosciences and the like, provide accurate brain space position information for an encephalic electroencephalogram signal, and enhance application value in the aspect of electrophysiological basis research.

A system including a hierarchical analytics framework that can utilize a first set of machine learned algorithms to identify and quantify a set of biological properties utilizing medical imaging data is provided. System can segment the medical imaging data based on the quantified biological properties to delineate existence of perivascular adipose tissue. The system can also segment the medical imaging data based on the quantified biological properties to determine a lumen boundary and/or determine a cap thickness based on a minimum distance between the lumen boundary and LRNC regions.

A method of transmitting medical imaging data files over high latency low bandwidth networks by using an incremental RSYNC protocol over an encrypted connection.

The invention discloses a retrieval system based on a multi-lesion region characteristic and oriented to a medical image database. In the invention, the image is omni-directionally described by advanced image description symbols, including image region contents, pathological representation and anatomical position information, thus realizing the quick positioning and matching method on a plurality of lesion regions; by adopting the method of combining the semantic navigation and high-dimension data index, the quick retrieval of large-scale image characteristic value can be realized, the retrieval efficiency is improved, and the 'semantic gap' phenomenon existing in the traditional image retrieval technology is relieved to certain degree. The retrieval system sufficiently utilizes the historical image and diagnosis data in a PACS (picture archiving and communication system) database, can be used as an effective measure for computer aided diagnosis, and can be widely applied to the fields such as medical clinics, researches and teaching.

A system and a method for converting an analog signal to a digital signal are provided. The technique involves receiving a sampled analog signal, and selecting one of a plurality of segments of a segmented relation between DAC output values and desired ADC input values. Desired gain and offset values are applied to the DAC output values or to the sampled analog signal based upon the selected segment. The sampled analog signal is converted to a digital signal based upon the desired gain and offset values.

The invention belongs to the technical field of computer application, specifically relates to the technical field of medical image analysis and specifically relates to a medical image data analysis method and system based on a fused deep tensor nerve network. The method is characterized by, to begin with, integrating effective information in medical images through a tensor convolutional neural network, and then, entering a tensor recursive neural network; and by carrying out analysis on historical medical image data and current medical image data of a patient, outputting an analysis result ofthe current medical image data and analysis result prediction of medical images in the future for providing analysis reference for doctors and evaluating a treatment scheme received by the patient respectively. Compared with a conventional recursive neural network, the network in the invention introduces tensor CP decomposition and tensor column decomposition, and the parameter scale of the tensorrecursive neural network is far smaller than the parameter scale of the conventional network in processing the same tensor data; and therefore, the medical image data analysis method and system can effectively improve reliability and efficiency of image analysis, and provide basis for adjustment and optimization of the treatment schemes.

A system and method for streaming unprocessed medical image data from a medical imaging system to a remote terminal is provided. A medical imaging system acquires medical image data, generates unprocessed medical image data, and then transmits the unprocessed medical image data to a remote terminal. The remote terminal receives the unprocessed medical image data, processes the data to render a medical image and displays the medical image to an operator at the remote terminal. Additionally, the operator may control imaging parameters at the remoter terminal for use in rendering the medical image. Additionally, the operator may control imaging parameters on the medical imaging system. Also, the operator at the remoter terminal and the operator at the medical imaging system may communicate with each other during the examination through the medical imaging system.

The invention discloses a medical big data classification method and system based on a generative adversarial network and semi-supervised learning, and the system comprises a data collection module which is used for collecting medical big data, and obtaining a large amount of medical data and medical images with high data dimension and high class mark uncertainty; The data processing module is used for preprocessing the acquired medical data and medical images; The algorithm application module is used for initializing and training the sub-learners, marking the unlabeled medical data and the unlabeled medical images, and expanding the labeled medical data and the labeled medical images; And the auxiliary decision module is used for classifying the medical big data of the test set. The dataprocessing module further comprises a medical data dimension reduction module, an image processing module, a data classification module and a medical data processing module; The algorithm applicationmodule further comprises a training sample generation module, a training module, a marking module, an expansion module and an integration module. And the accuracy of medical big data classification isimproved.