307 results about "Medical imaging data" patented technology

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The invention belongs to the technical field of medical image processing, and particularly relates to a linear Transform general focus recognition method based on multiple perception and context guidance, and the method comprises the steps: extracting the multi-scale features of a medical CT image through a backbone network, obtaining the edge gradient features in parallel, making up the missing of focus boundary information through an edge perception feature enhancement module, and carrying out the recognition of the focus. And then global feature modeling under linear complexity is realized through a polarity perception feature interaction module, a high-discrimination-force multi-scale feature map is generated by using a context-guided feature pyramid network, and finally the model is optimized by combining a Hungary algorithm and a joint loss function based on an end-to-end detection architecture of set prediction. The problems that the focus boundary is fuzzy, feature interaction and calculation efficiency are balanced, and the focus and background separation degree is weak are effectively solved, double improvement of calculation efficiency and detection precision on massive medical image data is achieved, and reliable support is provided for clinical precise auxiliary diagnosis.

The invention discloses an image processing method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining to-be-processed three-dimensional medical image data, carrying out the spinal segmentation of a spinal region in the three-dimensional medical image data, and obtaining vertebral mask data; and performing cone identification processing on the cone mask data to obtain a target end cone mask corresponding to the Cobb angle, constructing a space vector model for the target end cone based on the target end cone mask, and determining a Cobb angle value for the target end cone based on the space vector model. According to the method, the Cobb angle is calculated by introducing space vector modeling, so that the measurement error of measurement of the Cobb angle can be remarkably reduced, and the accuracy of measurement of the Cobb angle is improved.

The invention relates to the technical field of image modeling, in particular to a three-dimensional modeling method and system for medical image data, and the method comprises the steps: carrying out the preprocessing of the obtained two-dimensional or three-dimensional medical image data, extracting a blood vessel region based on the image texture and gray features, and generating a preliminary blood vessel structure segmentation image; in combination with a blood flow simulation or real acquisition means, obtaining dynamic feature data from the vascular structure segmentation map, and mapping the dynamic feature data to a corresponding vascular structure position to form a vascular distribution map with blood flow constraint; and inputting the blood vessel distribution diagram into a three-dimensional modeling module, expanding and complementing small blood vessel branches by utilizing blood flow constraint, dynamically correcting the form of a main blood vessel, and generating a three-dimensional blood vessel structure model with real fluid physiological characteristics. The method can be widely applied to medical research and clinical aid decision-making scenes such as preoperative simulation, individualized intervention path planning and blood flow reconstruction analysis, and has good popularization and application prospects and engineering practical value.

The invention relates to the cross technical field of medical education and mixed reality technology, in particular to a traditional Chinese medicine meridian point visualization system based on mixed reality technology, which comprises a data fusion and personalized calibration module, a virtual-real fusion interaction module, an interaction teaching module, an intelligent evaluation module and a meridian qi and blood simulation module. By fusing medical image data, depth sensing data and a traditional Chinese medicine acupoint selection rule, a personalized three-dimensional human body model is constructed, and acupoints are accurately calibrated; real-time registration and shielding processing of the virtual model and a real scene are achieved through mixed reality equipment; various natural interaction modes such as gestures, voices and sight lines are supported, and an integrated function of learning, training and assessment is provided; and the meridian qi-blood running state can be dynamically simulated based on the midnight-noon ebb-flow theory. The system has the advantages that the problems that traditional Chinese medicine teaching is poor in intuition and interactivity, cannot be personalized, is inconvenient to examine and the like are solved, and immersive, interactive and intelligent teaching of traditional Chinese medicine meridian point knowledge is achieved.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

Various embodiments include methods for generating patient-specific heart and thorax models using anatomical landmarks and segmentation data, optimized through the application of trained neural network models. Three-dimensional medical imaging data may be processed by a trained neural network to automatically segment and isolate the heart, blood cavities, and thorax to extract feature maps from the segmented images. Anatomical landmarks, such as the heart apex and valve centers, are identified and the alignment of heart axes is verified. Reference heart and thorax models are selected and adapted to fit the patient-specific landmarks through scaling, translating, and rotating. Best adapted heart and thorax models may then be used for conducting one or more medical procedures. Neural network models, trained on historical patient data sets, may be refined through machine learning from new patient data, thereby improving accuracy.

The invention provides a thyroid-related eye disease intraorbital soft tissue volume measurement method and system, and is applied to the technical field of data processing. Three-dimensional medical image data and system abnormal state information are obtained, the three-dimensional medical image data comprise CT images and MRI images, and the system abnormal state information comprises artifacts, isolated noisy points and irregular boundaries; data preprocessing is carried out on the three-dimensional medical image data, standardized image data is generated, and the standardized image data is formed by carrying out ternary processing on spatial registration, noise suppression and gray level normalization; processing the standardized image data and the system abnormal state information to generate an initial segmentation mask; processing the initial segmentation mask to generate an optimized mask; processing the optimized mask and the three-dimensional medical image data to generate soft tissue volume data; and processing the soft tissue volume data, the system abnormal state information and the historical volume data to generate a volume dynamic change report.

The invention discloses a medical image diagnosis method based on a hierarchical differential privacy federated learning method. The method comprises the following steps: constructing a medical institution federated learning network system; preprocessing the collected medical image data and dividing a data set; initializing a deep neural network diagnosis model architecture and gradient sensitivity evaluation parameters; calculating a local gradient and a hierarchical gradient variance of the client; dynamically evaluating the hierarchical gradient sensitivity; the server aggregates the sensitivity and updates the dynamic threshold; implementing layered and differentiated privacy budget distribution; the client receives the feedback signal and adjusts the learning rate; dynamically adjusting the local privacy budget; differential privacy noise is injected into the gradient; uploading the noisy gradient to a server and updating the global model; calculating verification loss and generating a feedback signal; adaptively adjusting the global privacy budget; broadcasting the updated model and parameters; iterative training is carried out until convergence; deploying the final diagnosis model to each medical institution to execute medical image real-time diagnosis; the defects of a medical image system are overcome.

The invention discloses an intelligent tumor diagnosis large model construction method based on multi-modal medical data fusion. The method comprises the steps of 1, data collection processing and feature extraction, 2, establishment of a dynamic fusion model, 3, arrangement of a training set, 4, model training optimization, 5, establishment of an auxiliary diagnosis model and 6, model deployment. According to the method, a three-level fusion mechanism of a self-adaptive fusion framework is established, medical image data and clinical data of various modes are effectively fused, the model is trained by using a double-domain transfer learning strategy, the small sample learning efficiency and precision are improved, the characteristics of tumors can be reflected more comprehensively and accurately, and the method is suitable for large-scale popularization and application. A powerful auxiliary reference is provided for improving the accuracy and efficiency of tumor diagnosis, and the model can be expanded and deployed in other disease auxiliary diagnosis reference systems and has a wide application prospect.

The embodiment of the invention provides a medical progressive diagnosis method based on dynamic visual focusing, and belongs to the technical field of medical care informatics, and the method specifically comprises the steps: 1, obtaining medical image data and clinical query data; 2, constructing a hybrid reinforcement learning framework, and training a network model through a bimodal exploration mechanism and a double-flow optimization mechanism based on medical image data and clinical query data to obtain a trained diagnosis model; and step 3, inputting the to-be-diagnosed medical image and the corresponding clinical query into the diagnosis model, and outputting a diagnosis report containing the progressive visual focusing trajectory and the final diagnosis result. Through the scheme disclosed by the invention, the expert-level diagnosis thinking can be simulated to realize accurate diagnosis without relying on large-scale marking reasoning data, the problems of strategy collapse and visual illusion of pure online learning are effectively solved, and the diagnosis accuracy and interpretability are improved.

The invention provides a pancreatic cancer multi-modal hierarchical fusion prognosis prediction system and method.The system comprises a data input module, a preprocessing module, a multi-modal feature fusion module and a risk prediction module, and the data input module is used for receiving and managing medical image data, clinical table data and text report data of a patient; the preprocessing module is used for performing standardization processing and feature extraction on input medical image data, clinical table data and text report data; the multi-modal feature fusion module is used for fusing the image features and carrying out hierarchical fusion on the multi-modal features; and the risk prediction module is used for calculating a prognosis prediction result based on the multi-modal characteristics and providing support for clinical decision-making of pancreatic cancer. According to the method, the risk stratification of the patient can be accurately carried out, and a powerful basis is provided for clinically formulating a personalized treatment scheme.

The invention provides a MaskRcnn-based tumor detection method and system, and the method comprises the steps: inputting medical image data into a MaskRcnn network integrated with an edge perception module, generating an edge response graph through a Sobel operator, and enabling the edge response graph to serve as an additional channel injection feature graph, and enhancing the boundary representation; fusing multi-scale features through a feature pyramid network FPN, extracting candidate tumor area features through RoI Align, introducing non-local attention modeling global dependence in the area, extracting directional entropy, texture energy, uniformity and contrast in combination with a gray level co-occurrence matrix GLCM, and splicing to generate structure sensing features; performing classification, bounding box regression and mask segmentation based on the structure perception features, and constructing a joint loss function including classification, regression, mask cross entropy, edge alignment and structure consistency; the positioning capability of the model on the fuzzy tumor contour is effectively improved, the boundary positioning error is remarkably optimized, and the defect that over-segmentation or missing detection is likely to occur in a traditional model is overcome.

The invention discloses an intelligent medical system based on multi-modal data fusion, and the system comprises a multi-modal data feature extraction module which is used for extracting the internal information of medical image data Fimg, pathological section data Fpath, text case data Ftext and voice case data Faudio; the inter-modal relationship analysis module is used for obtaining a visual consistency feature Fvis and a first association intensity mark of image-pathology fusion, obtaining a language consistency feature Flang and a second association intensity mark of text-voice fusion, and obtaining a key association feature Fcross representing the association intensity of vision and language and a third association intensity mark; and the fusion module is unified to obtain final judgment data. Aiming at the common problem of image and pathology inconsistency in medical practice, the method not only records consistency information, but also systematically analyzes various aspects of inconsistency, and provides more comprehensive reference for clinical decision making.

This invention discloses a lesion localization method and system based on deep learning, comprising: acquiring medical image data; preprocessing the images to obtain standardized data; inputting the standardized data into an improved nnFormer to construct a potential lesion morphological field; fusing the morphological field and image data to generate a multi-source lesion mechanism parameter field; segmenting the lesion occurrence mechanism parameter field, establishing evolution paths, and inversely reasoning about boundary regions; calculating topological and physiological characteristics, and jointly assessing the final lesion region using energy; and outputting the lesion localization result, including spatial location and regional extent. This invention achieves stable and accurate localization of lesion regions in medical images by constructing a potential lesion morphological field from standardized medical images using an improved nnFormer, and by fusing multi-source mechanism parameter fields, morphological evolution inverse reasoning, and topological-physiological joint energy assessment.

The application relates to the technical field of high-intensity focused ultrasound treatment, and discloses an energy mode switching and treatment path planning method and system based on distance constraints, which comprises the following steps: segmenting and three-dimensionally reconstructing medical image data to obtain a three-dimensional model; generating a grid about a treatment point in a tumor area of the three-dimensional model, and binding a corresponding lesion physical property label to each treatment point; calculating the shortest distance from each treatment point to a key tissue to form an enhanced treatment point set; inputting the enhanced treatment point set into a preset decision model to obtain a final treatment point set; grouping the treatment points in the final treatment point set based on energy mode labels; and performing spatial clustering on the treatment points in each group to form multiple treatment clusters and generate a complete treatment path sequence. The application reduces process loss caused by mode switching, and makes the treatment process more systematic and controllable.

A system (100) may receive three-dimensional (3D) medical imaging data of a region of interest of a subject. The system (100) may generate a first volume rendered image of an anatomical structure of a region of interest of a subject, and display the first volume rendered image. The system (100) may receive user input that selects a position of the virtual light source relative to the anatomical structure in the first volume rendered image. The system (100) may determine respective illumination parameters of the anatomical structure based on a position of the virtual light source relative to the anatomical structure of the region of interest. The system (100) may generate a second volume rendered image by removing one or more of the anatomical structures from the first volume rendered image based on respective illumination parameters of the anatomical structures. The system (100) may simultaneously display the first volume rendered image and the second volume rendered image.

The invention provides a medical image intelligent labeling and auditing method based on deep learning, and relates to the technical field of medical image labeling, and the method comprises the steps: carrying out the preprocessing of medical image data, and obtaining the preprocessed image data; performing two-dimensional evaluation on the preprocessed image data based on image complexity and labeling task complexity to obtain a total complexity score; dividing the preprocessed image data into a simple level, a medium level and a complex level according to the total complexity score; carrying out labeling processing on the preprocessed image data by adopting a differential labeling strategy to obtain a labeling result; performing quantitative evaluation on the labeling result to obtain a quality score; extracting labeling process features, and dividing labeling results into high quality, medium quality and low quality; and determining an auditing strategy, auditing the annotation result, and outputting the annotation result which is audited to be qualified. According to the method, objective evaluation of labeling quality and reasonable configuration of auditing resources can be realized, and the consistency of auditing is guaranteed.

The invention discloses an end-to-end orthopedic medical image segmentation and three-dimensional reconstruction method and system, electronic equipment and a computer readable medium, and belongs to the field of medical image processing, and the method comprises the following steps: S1, obtaining orthopedic three-dimensional medical image data, and carrying out standardized preprocessing to obtain a standard medical image set; s2, constructing an improved 3D (three dimensional)-Trans UNet network architecture; s3, performing network training based on a multi-task cooperative training strategy; s4, performing post-processing optimization on the three-dimensional model to obtain a segmentation body mask in a standard format and a regularized three-dimensional model; and S5, system integration and clinical deployment. By adopting the end-to-end orthopedic medical image segmentation and three-dimensional reconstruction method and system, the electronic equipment and the computer readable medium, integrated efficient execution of orthopedic medical image high-precision segmentation and regular three-dimensional reconstruction is realized.

To provide an imaging processing system that improves utilization efficiency of an FOV part in an imaging area.SOLUTION: A system of the present invention displays medical imaging data, and includes one or more data inputs, one or more processors, and one or more displays. The one or more data inputs are configured to receive first image data generated by a first medical imaging device. The first image data includes a field-of-vision (FOV) part and a non-FOV part (Step 202). The one or more processors identify the non-FOV part of the first image data (Step 204). By removing at least part of the non-FOV part of the first image data, cropped first image data is generated (Step 206). The cropped first image data to be displayed in a first part of the display and additional information to be displayed in a second part of the display are transmitted (Step 208).SELECTED DRAWING: Figure 2

The invention discloses a multi-modal medical image analysis system and method, and the system comprises a data obtaining and feature extraction module which is used for obtaining medical image data and clinical data of a patient, and extracting multi-modal features from the medical image data and the clinical data; the feature fusion and standardization module is used for performing decision-level fusion on the multi-modal features and performing standardization processing on the basis of pre-stored training set statistics so as to eliminate dimensional differences among different features and overcome the limitation of dependence on single-modal data in the prior art; and the multi-task prediction module is internally provided with a well-trained multi-task prediction model, can input the standardized multi-modal feature vectors at the same time, synchronously outputs prediction results of the muscular infiltration depth, histological type and molecular typing of the endometrial cancer, and outputs probability distribution corresponding to each prediction result at the same time. A built-in multi-task prediction model of the system adopts three independent parallel prediction paths, so that the comprehensiveness and the refinement degree of endometrial cancer preoperative evaluation are greatly improved.

The invention discloses a method, a device and a system for planning an SEEG electrode implantation path. The method comprises the following steps: processing medical image data of the brain of an epileptic to obtain a target brain anatomical image; matching and searching a target experience module in an experience database according to the clinical description data of the epileptic; the experience database is established according to prior experience, a plurality of experience modules are arranged in the experience database, and each experience module comprises a plurality of combinations of a first target range and a second target range of a fixed electrode; and determining a target path of each electrode based on the target brain anatomy image and the target experience module. According to the method, the experience database containing a plurality of experience modules is established, the target experience module is screened in combination with the clinical description data of the patient, the target path of each electrode is further screened in combination with the target brain anatomy image of the patient, the proper path of the SEEG electrode is accurately and efficiently planned for the individual patient, and the accuracy of the SEEG electrode is improved. The method provided by the invention can reduce the learning and use threshold of doctors, and has high popularization value.

This application provides a method and system for modeling multi-class lung airway tree models based on shape-aware sampling. The method includes: acquiring three-dimensional medical image data containing multiple types of tracheal anatomical annotations and determining a training set; determining sampling patches for the images in the training set based on a fractal dimension-driven sampling patch adjustment strategy; determining a skeleton-weighted loss term based on the training set using a shortest path cost skeletonization method; determining a total loss function based on the skeleton-weighted loss term and volume overlap loss; training a pre-defined neural network for multi-class lung airway tree modeling based on the sampling patches and total loss function of the images in the training set; and modeling the three-dimensional medical image of the lung to be modeled using the trained multi-class lung airway tree modeling model. This application effectively improves the volume overlap accuracy and topological consistency of multi-class tracheal model reconstruction, alleviating branch breakage and topological errors in discrete voxel space.

A medical imaging apparatus includes processing circuitry to receive a medical imaging data set comprising intensity values as a function of position; obtain a segmentation of a bone region represented in the medical imaging data set; process the medical imaging data set to identify a plurality of sub-regions within the bone region for which intensity values are within a threshold intensity range with respect to a calibration intensity value; and determine concentration and / or distribution of the sub-regions within the bone region.

The application belongs to the technical field of medical image processing, and particularly relates to a kind of bone fracture block automatic reduction methods based on medical image;Including: obtaining the three-dimensional medical image data and three-dimensional label data after fracture of the object to be processed, and pre-processing the three-dimensional medical image data after fracture;The three-dimensional medical image data after fracture is input into the fracture surface segmentation model trained, and the main bone surface area and fracture block surface area are obtained;Three-dimensional coordinates are extracted and main bone surface point cloud and fracture block surface point cloud are constructed;Iterative closest point registration is carried out on the two kinds, and the cumulative rigid body transformation parameter is obtained;The voxel coordinate set corresponding to the fracture block is determined, and the voxel coordinate set is subjected to spatial transformation using the cumulative rigid body transformation parameter, to obtain the three-dimensional label result after reduction;The application can simultaneously complete the problems of automatic identification of fracture section and automatic solution of fracture block reduction posture, improve the degree of automation, result stability and interpretability of fracture block reduction process.

The application discloses a blood flow guiding dense mesh stent preoperative prognosis evaluation method and device based on hemodynamics, equipment and a storage medium, which comprises the following steps: constructing a three-dimensional blood vessel geometric model based on medical image data of a patient, performing hemodynamic simulation, obtaining a jet path in a tumor cavity, virtually implanting the blood flow guiding dense mesh stent in the three-dimensional blood vessel geometric model, obtaining a stent center line of the stent located in the tumor cavity, calculating a jet compliance index for prognosis evaluation based on the jet path in the tumor cavity and the stent center line, constructing a jet compliance comprehensive index based on the first jet compliance index, the second jet compliance index and the third jet compliance index, and performing prognosis evaluation on the preoperative curative effect of the blood flow guiding dense mesh stent according to the comprehensive index, so that the reliability of prognosis prediction is improved.

The application discloses a cardiovascular chronic disease data management method based on artificial intelligence, relates to the technical field of artificial intelligence medical management, and comprises the following steps: collecting patient multi-element heterogeneous medical data, normalizing and converting structured and semi-structured data, extracting key features of unstructured medical image data and vectorizing and encoding the key features, and generating a standardized cardiovascular chronic disease comprehensive data cube; calling a pre-trained multi-modal cardiovascular risk assessment model to analyze the data cube, and generating a comprehensive risk assessment report containing a risk level, a contribution factor, an evolution trend and an individualized early warning threshold; combining a clinical path knowledge base to generate an individualized health management plan, continuously collecting plan execution feedback data, dynamically updating the data cube, and iteratively optimizing the assessment report and the management plan. The method realizes multi-element heterogeneous data regularization, enriches risk assessment dimensions, realizes dynamic adaptation of a health management plan, and improves the standardization and individualization level of cardiovascular chronic disease data management.