811 results about "Computed tomography" patented technology

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

This disclosure relates to systems, methods, and devices for plaque analysis, vessel and fluid flow analysis, and / or risk determination or prediction thereof. Some embodiments relate to determining fractional flow reserve (FFR) values. Some embodiments relate to determining FFR values using 3D-printed models. Some embodiments relate to identifying thin cap fibroatheroma based on analysis of computed tomography (CT) imaging. Some embodiments relate adjusting calcified plaque thresholds for CT images to address effects of calcium blooming.

A system for pancreatic cancer image segmentation based on a multi-view feature fusion network, comprising inputting acquired computed tomography (CT) image into network model, extracting shallow features of CT image to obtain shallow feature map; reconstructing the shallow feature map into code sequence and inputting into lightweight Transformer for extracting global dependency relationship; carrying out operations on the shallow feature map to obtain first feature map; fusing the global dependency relationship and the first feature map to obtain second feature map; discarding redundant information from feature map obtained through convolution operation of each layer of the network, and regarding top-level feature map with discarded redundant information as third feature map; and carrying out pooling operations on the third feature map to obtain predicted boundary circle of target region, adding regularization item to the predicted boundary circle to obtain reference boundary circle, then outputting result.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention relates to the field of intelligent irrigation, and discloses an intelligent irrigation system and method for a crop root system, and the system comprises a root zone sensing module which is used for collecting soil environment parameters through multi-mode soil sensor arrays disposed at soil layers of different depths, generating a sensing matrix, and transmitting the sensing matrix to a root zone sensing module; the sensing matrix is used for representing water potential, pH value, nutrient concentration and temperature information in each soil layer; and the root system density analysis module is connected with the root zone sensing module, and is used for constructing a root system density weight matrix based on root system space distribution information obtained by underground computed tomography, and multiplying the sensing matrix by the root system density weight matrix element by element to generate a root zone state vector. Accurate generation of regulation and control signals is achieved, the micro-fluidic execution module can adjust the irrigation flow and the fertilization proportion in real time according to the actual requirements of the root zone, and therefore it is guaranteed that the rhizosphere environment is always close to the optimal physiological interval.

Provided is a program, etc. capable of acquiring information related to an amount of body tissue from an X-ray image with high accuracy using a small number of cases. A computer acquires training data including an X-ray image of a target site and information related to an amount of body tissue obtained from a CT (Computed Tomography) image of the target site. The computer generates a learning model configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input using acquired training data.

The invention discloses a composite material stress distribution analysis method and system and a storage medium. The method comprises the following steps: acquiring a defect detection image obtained by computed tomography or infrared thermal imaging; identifying a defect area in the composite material structure and extracting defect information; according to the defect information and the loading direction, calculating a defect-induced stress enhancement factor and a stress transfer function, and constructing a stress correction factor distribution diagram; establishing a reference finite element model ignoring defects, and calculating a reference stress distribution field; acting the correction factor distribution diagram on the reference stress field to generate a correction stress distribution diagram; combining defect identification errors and material statistical fluctuation to construct an uncertainty propagation model, quantifying the corrected stress diagram, and outputting a stress value interval map; and finally, jointly correcting the stress map and the interval map, and identifying a high-risk area.

A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The invention discloses a plane CT geometric calibration method, system, storage medium and device, and the method comprises the following steps: obtaining a plane CT to scan a calibration model body, so as to obtain a projection image of the calibration model body; the calibration die body comprises a body and a plurality of pairs of calibration small balls arranged on the body, and the plurality of pairs of calibration small balls are configured to be of a two-layer structure; wherein a sphere center connecting line of each pair of calibration small balls is perpendicular to a detector receiving plane of the plane CT; processing the obtained projection image to obtain projection coordinates of each calibration small ball; pairing the calibration balls based on the projection coordinates of each calibration ball, and determining a projection coordinate connecting line of each pair of calibration balls; determining vertical point coordinates of a radiation source focus in the plane CT in the receiving plane of the detector based on each projection coordinate connecting line; and adjusting the position of the radiation source or the detector to enable the vertical point of the radiation source to coincide with the plane center point of the detector, thereby completing calibration. The method is easy to operate, the plane CT geometric calibration precision can be effectively improved, and the calibration workload is reduced.

The invention discloses a CT (Computed Tomography) image pulmonary embolism segmentation and classification method combined with quality evaluation, which relates to the technical field of image processing, and comprises the following steps: inputting a 256 * 256 pulmonary embolism CT image and a quality score thereof into a quality score guide encoder, expanding a quality score dimension through linear transformation, and carrying out point product fusion with a feature map extracted by ResNet34 layer by layer to obtain a final product; generating multi-scale coding features; performing wavelet domain decomposition and reconstruction on the coding features through a wavelet transform fusion jump link module, and optimizing feature transmission; a multi-scale cross enhanced decoder is adopted to carry out multi-scale deconvolution fusion on the features, a segmentation result is output in combination with an efficient channel attention mechanism, meanwhile, pulmonary embolism existence judgment is output through a classification head, and the method provides powerful support for early diagnosis of pulmonary embolism, development of an image auxiliary diagnosis system and clinical application, and has good application prospects. Wide application prospects and profound social significance are realized.

The invention provides a single ear grain extraction method and device based on three-dimensional CT data. According to the method, the stem area in the plant three-dimensional CT data can be removed to obtain the stem-removed three-dimensional data, interference of the stem area on ear grain extraction is avoided, the area with the gray value larger than or equal to the gray threshold value in the stem-removed three-dimensional data is reserved, the remaining area is removed, and therefore the accurate ear grain three-dimensional data is obtained, and the data comprises a plurality of ear grains. Each communication area determined through communication analysis corresponds to one ear grain area, the communication areas are matched with corresponding labels respectively, and then different ear grain areas are distinguished through different labels; according to the method, a single ear grain is extracted according to a label to obtain a single ear grain mask, and an accurate single ear grain three-dimensional image is extracted from plant three-dimensional CT data by using the single ear grain mask, so that the accuracy and the stability of extraction of the single ear grain three-dimensional image are remarkably improved, and phenotype analysis and quality detection according to the single ear grain three-dimensional image are facilitated.

The invention relates to the technical field of medical imaging, and discloses a method for automatically discriminating and enhancing phases in CT scanning by an attention mechanism based on multi-instance learning, and the method comprises the steps: extracting a CT scanning image and DICOM information from a data set, and unifying the image size after HU conversion, truncation and linear zooming; obtaining a fixed number of slices by using direct or indirect random sampling and a cubic interpolation strategy; then, a pre-trained model is adopted to extract slice features, scanning level feature vectors are formed through attention aggregation, and finally prediction probabilities of four types of phases are output through a full connection layer and a Softmax module; the model is trained by adopting cross entropy loss and an Adam optimizer, the performance is evaluated through indexes such as precision, recall rate and F1-score, and a thermodynamic diagram is generated by combining gradient back propagation, so that visual visualization of a result is realized, and clinical judgment is assisted. According to the method, the CT scanning multi-phase can be accurately, rapidly and interpretably classified in a real clinical environment.

An automated clinical decision support method for treatment re-planning that enhances a CBCT scan using information from an initial CT scan of the patient to create a synthetic CT scan; adapts contours of organ(s) and tumour(s) from the initial CT scan to align with the synthetic CT scan; and calculates a delivered radiation dose from the synthetic CT scan; compares these against an original treatment plan to detect deviations exceeding preset thresholds, and triggers alerts with assigned priority levels for a re-plan decision.

The invention relates to the technical field of soil ecological monitoring, in particular to a coastal saline soil dynamic monitoring and early warning system based on the Internet of Things, which comprises a collaborative monitoring unit, a fusion resolving analysis unit and an early warning trigger unit. The capillary water rising flux and the underground water seepage direction are synchronously monitored, the fusion resolving analysis unit dynamically corrects the capillary conductivity through a pore scale migration model in combination with three-dimensional pore structure parameters reconstructed through soil CT scanning, the salt exchange capacity is resolved, the salt increment is predicted through a long and short-term memory network, and a decision coefficient is calculated. The early warning trigger unit judges the secondary salinization risk when the conditions are met, activates drainage equipment and pushes early warning, the problems that traditional monitoring dimensions are single and the early warning precision is insufficient are solved, and the method is suitable for dynamic monitoring and risk prevention and control of coastal salinized soil.

The invention relates to the technical field of medical image processing, and particularly discloses an intelligent segmentation system for a kidney tumor in a CT image, and the system comprises an image preprocessing module, an ROI automatic detection module, a kidney tumor segmentation module, a segmentation result post-processing module, a visual interaction module, and a model updating module. The system improves image quality through normalization and filtering, uses a target detection network to position a kidney area, realizes precise tumor segmentation based on an integrated channel attention mechanism and a U-Net network guided by structure priori, optimizes a boundary in combination with a conditional random field, adopts a multi-plane fusion strategy to improve three-dimensional consistency, and calculates tumor volume. The system supports federal learning update, improves the cross-mechanism generalization ability, has the advantages of high segmentation precision, strong boundary reducibility, good structural rationality, strong clinical deployment and the like, and is suitable for kidney tumor auxiliary diagnosis and quantitative analysis.

The invention relates to the technical field of medical image intelligent diagnosis, and provides an interpretable lung CT image intelligent diagnosis method and device, and the device comprises an image calling module, an image processing module, an image analysis module and a result display module. According to the method, identity information of a patient is read to automatically verify and authorize, and an electronic CT image is called and stored in a data storage card through an encryption transmission channel. And the image processing module performs denoising, enhancement and interpretability analysis on the original image to generate a thermodynamic diagram to assist in decision making. The image analysis module completes lesion detection and classification, verifies the consistency between a model decision and doctor experience through a thermodynamic diagram, and optimizes network parameters in combination with feedback data. And the result display module displays a diagnosis result and a thermodynamic diagram in real time, and supports multi-terminal cooperation and data archiving. The device supports data sharing and remote consultation, the efficiency and convenience of diagnosis are improved, and the wide application prospect of artificial intelligence in the field of medical diagnosis is shown.

The invention discloses a chronic sinusitis CT (Computed Tomography) image automatic typing and severity assessment method and a chronic sinusitis CT image automatic typing and severity assessment system based on deep learning. The method comprises the following steps: acquiring paranasal sinus CT image data of a chronic sinusitis patient and an associated clinical tag; carrying out standardization processing on the CT image, wherein the standardization processing comprises gray level normalization, denoising and region-of-interest segmentation; a multi-task deep learning model is constructed, an improved 3D Re < s> Ne < t-50 > architecture is adopted by a typing sub-model to output an immune endotype classification result, and a severity evaluation sub-model segments a lesion area based on a U-Ne < t + + > structure and automatically calculates a severity level; integrating model output to generate individualized treatment suggestions; and superposing a labeling result through the thermodynamic diagram and embedding the labeling result into a hospital PACS system. Accurate typing and evaluation of chronic nasosinusitis are achieved, and diagnosis efficiency and clinical decision support capability are improved.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

A system and method includes acquisition of nuclear imaging data, reconstruction of a three-dimensional image based on nuclear imaging data, input of the three-dimensional image to a trained convolutional network to either generate a linear attenuation correction map and a simulated computed tomography image based on the linear attenuation correction map or generate simulated computer tomography images directly, application of a segmentation algorithm to the simulated computed tomography image to generate a segmentation map, and display of the segmentation map.

The invention belongs to the technical field of deep learning, and particularly relates to a kidney and kidney stone segmentation method for a computed tomography image and electronic equipment. Comprising the following steps: S1, carrying out data preprocessing and data enhancement on a CT image data set; s2, constructing a semantic segmentation model for segmenting the CT image; s3, constructing a composite loss function fusing a cross entropy loss function and a loss function based on a Dice coefficient for training the semantic segmentation model; s4, setting a learning rate change curve, performing 2D slicing on the CT image data, and training a semantic segmentation model by using the 2D slices; and S5, segmenting the kidney and the kidney stone in the CT image by using the trained semantic segmentation model. According to the method, data preprocessing and data enhancement are performed by combining data set statistical parameters, Attention-gate and residual structures are introduced to construct the segmentation model, and finally, a proper training and prediction strategy is adopted, so that the segmentation accuracy of the kidney and the kidney stone in the CT image is improved.

The present invention discloses a vascular segmentation model and method. The vascular segmentation model includes an encoder and a decoder. The encoder extracts features from computed tomography images to obtain a basic neural network feature map. The decoder includes a vascular perception module and an upsampling layer. The vascular perception module fuses prior knowledge of blood vessels with the basic neural network feature map to output a vascular segmentation map. The upsampling layer restores the vascular segmentation map processed by the vascular perception module to a size corresponding to the CT image. The technical solution of this application can effectively improve vascular segmentation accuracy.

The invention discloses a pig organ automatic segmentation method and segmentation system based on a CT anatomical structure relationship. The method comprises the following steps: acquiring a whole-body CT scanning image of a live pig; and processing the pig whole body CT scanning image by using a pig multi-organ automatic segmentation model to obtain a pig multi-organ prediction mask, and completing automatic segmentation of the multiple organs of the pig. The live pig multi-organ automatic segmentation system comprises a live pig CT image acquisition module and a multi-organ automatic segmentation module, wherein the live pig CT image acquisition module is used for acquiring a to-be-segmented live pig CT image; the multi-organ automatic segmentation module comprises a visual state space block-based encoder-decoder architecture, a spatial link GRU module, a global organ category encoding module, a global organ category guiding module and a semi-supervised training framework. According to the method, multiple organs in the CT image of the live pig can be segmented efficiently and accurately, the problems of low segmentation speed and low precision of a traditional method are solved, and a more efficient solution is provided for medical image analysis and animal husbandry management of the pig.

The invention relates to a semi-quantitative fluid inclusion analysis method based on three-dimensional CT (Computed Tomography) scanning, which is applied to the technical field of geological mineral detection and comprises the following steps: acquiring sample data of a sample to be analyzed; performing sample preparation on the to-be-analyzed sample based on the sample data to generate a target sample; performing three-dimensional CT scanning processing on the target sample to generate three-dimensional cross-sectional image data; performing image preprocessing on the three-dimensional cross-sectional image data to generate processed three-dimensional data; extracting a three-dimensional structure of the fluid inclusion based on the processed three-dimensional data to obtain three-dimensional data of the fluid inclusion; performing parameter calculation and semi-quantitative analysis on the three-dimensional data of the fluid inclusion to generate calculation and analysis data; and generating a fluid inclusion analysis result based on the fluid inclusion three-dimensional data and the computational analysis data. The method has the effect of comprehensively and accurately analyzing the fluid inclusion.

The invention discloses a metal internal characteristic selective extraction method, which comprises the following steps of: (1) scanning a block-shaped metal sample by adopting high-resolution X-ray CT (Computed Tomography), reconstructing characteristics including defect distribution, microstructure, holes and cracks in metal through a three-dimensional reconstruction algorithm, and generating a high-precision three-dimensional model; (2) selecting a required area, calculating voxel resolution according to the distance SDD between a radiation source and a detector and the distance SOD between a sample and the radiation source, displaying a reconstructed three-dimensional image in three-dimensional visual analysis software, and performing threshold segmentation through a computer vision algorithm, and automatically identifying and extracting required features for observation; the whole sample is placed in a square frame in an externally tangent form, and a Cartesian rectangular coordinate system is established. According to the invention, after the internal structure is observed by using the X-ray microscopic imaging technology, an interested area is selected for extraction by using linear cutting according to the three-dimensional structure information, and the internal defect of the sample can be efficiently and accurately positioned and extracted.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.