372 results about "Radiomics" patented technology

An intelligent pulmonary nodule grading method and system based on multi-modality feature fusion. The method comprises: using clinical multi-modality data of a physical examination crowd to acquire an ROI and a VOI of a pulmonary nodule on the basis of a chest CT examination image and an examination report, designing a multi-task feature extraction network based on an attention mechanism, and acquiring radiomics features and deep-layer image features from the ROI and the VOI; designing a cross-modality feature fusion method based on graph representation learning, designing a multi-modality information extraction method, acquiring a specific feature representation and graph structure of each modality, and then performing feature representation and graph structure fusion; and providing a pulmonary nodule grading GCN model optimization and clinical verification method based on self-supervised learning, so as to implement fine grading of pulmonary nodule malignancy having only slight differences. A new way is provided for the design of a fine-grained classification algorithm; and in the aspect of solving a clinical problem, provided is a classification algorithm optimization method and clinical verification scheme in which generative self-supervised learning and comparative self-supervised learning are organically combined.

The invention provides a correlation analysis method for a lung CT image, and the method comprises the steps: obtaining a lung multi-modal image which comprises a computed tomography image and a positron emission tomography image, and carrying out the denoising, enhancement and standardization preprocessing of an original image according to the imaging principles and image characteristics of different modals; extracting a lung lesion region of interest from the registered multi-modal image, sketching and segmenting the lesion, and quantitatively analyzing the volume and shape geometric morphological characteristics of the lesion; based on a curative effect result, correlation between radiomics features and patient prognosis is analyzed, recurrence and metastasis risks and lifetime under different feature expressions are estimated, a curative effect prediction model is constructed in combination with image features and clinical indexes, and long-term individualized evaluation is performed on patient prognosis.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a multi-task lung cancer brain metastasis lifetime prediction method based on multi-modal data fusion, and relates to the field of medical image processing, and the method comprises the steps: obtaining non-small cell lung cancer CT image data, gene data and clinical data; performing focus segmentation on the non-small cell lung cancer CT image data; feature extraction is carried out based on the focus segmentation result, and radiomics features and depth image features are obtained; performing feature extraction on the gene data to obtain gene features; missing value deletion and numeralization preprocessing are carried out on the clinical data to obtain clinical features; local cross-modal attention fusion is carried out on the radiomics features and the depth image features; performing global cross-modal attention fusion on the preliminary mixed image features, the gene features and the clinical features to obtain original mixed features; and performing conditional guidance diffusion based on the original mixed features to obtain a lung cancer brain metastasis classification judgment result and a lifetime prediction risk. According to the invention, the lifetime prediction accuracy and stability can be improved.

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

In the disclosed systems and methods for characterizing a cancer condition of a tissue in a subject, a computer system inputs information into an ensemble model. The information includes, for each respective class of radiomics features in a plurality of classes of radiomics features, a corresponding value for each respective radiomic feature in a corresponding plurality of radiomics features of the respective class of radiomics features obtained from a medical imaging dataset. The ensemble model comprises a plurality of component models. The computer system obtains as output from each respective component model in the plurality of component models a corresponding component prediction for the cancer condition, thereby obtaining a plurality of component predictions for the cancer condition. The computer system combines the plurality of component predictions to obtain as output of the ensemble model a characterization of the cancer condition.

The embodiment of the invention provides a multi-modal data fusion method and device based on a cross attention mechanism. The method comprises the following steps: acquiring biological information features and radiomics features of a target object; carrying out feature weighting and interactive processing on the biological information features and the radiomics features based on a cross attention mechanism, and generating the biological information features based on radiomics feature attention and the radiomics features based on the biological information feature attention; carrying out feature splicing on the four features to generate spliced features; and performing feature fusion on the spliced features based on a self-attention mechanism to generate fusion feature representation of the target object. The multi-modal data fusion method and device provided by the invention show higher flexibility and pertinence in the aspects of feature extraction and weighting.

The invention discloses an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

The invention relates to the technical field of medical image analysis, and discloses an HER2 positive breast cancer auxiliary prediction method based on multi-modal data fusion. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The invention relates to the technical field of image recognition, and discloses a carotid plaque stability and stroke risk prediction system fusing multiple modes, and the system comprises a data collection and fusion module, a feature extraction and association module, a risk assessment and layering module, an intervention decision module, and a report generation and feedback module. When carotid plaque stability and cerebral apoplexy risk prediction is carried out, plasma proteomics, radiomics and clinical data are synergistically integrated by establishing a multi-modal data fusion analysis framework, so that the limitation that a traditional method depends on a single data source is overcome; the plaque risk can be evaluated from multiple dimensions of biological activity and morphological features, the comprehensiveness and accuracy of risk prediction are improved, a more reliable diagnosis basis is provided for clinic, interaction and sensitivity influence among different modal features can be adaptively quantified by introducing dynamic feature correlation modeling and a real-time weight calibration mechanism, and the risk prediction accuracy is improved. And objectivity and consistency of risk assessment results are ensured.

The invention discloses a colorectal cancer metastasis risk prediction method and system fusing radiomics and clinical characteristics, and the method comprises the steps: S1, collecting image data and clinical risk factor data of a patient, carrying out the preprocessing, and carrying out the feature extraction; s2, performing data expansion, and fusing expanded features and original features; s3, constructing and training a multi-modal large model, and outputting a first prediction result of the colorectal cancer metastasis risk of the patient; s4, layering the patients according to the first prediction result, and performing dynamic prediction; s5, performing interpretable analysis on a dynamic prediction result and generating a report; and S6, verifying the multi-modal large model. According to the method, the aspects of data fusion, prediction performance, dynamic assessment, clinical interpretation and the like are remarkably improved, key problems in the prior art are solved, an efficient, accurate and easy-to-use transfer risk assessment tool is provided for clinicians, and the method has important academic value and wide clinical application prospects.

The invention discloses a tumor risk layering method and system based on multi-modal imaging omics, and is applied to the technical field of medical image processing, and the method specifically comprises the steps: delineating a region of interest of a tumor according to the clinical medical information of a patient; extracting radiomics features in the region of interest; screening the image omics characteristics to obtain updated image omics characteristics; inputting the updated radiomics features into a risk hierarchical prediction model for training to obtain a tumor risk prediction model; the non-invasive risk stratification prediction of tumor patients can be realized, good sensitivity and specificity can be achieved, and the high false negative rate existing in the current clinical technology is reduced.

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 discloses an ovarian cancer preoperative accurate prediction system and method based on multi-modal imageomics characteristics, and the method comprises the steps: taking a US image, a CT image and an MRI image as the basis, carrying out the radiomics characteristic extraction of the three types of modal image data, and carrying out the combined screening of the three types of modal image data, so as to determine a matched classifier, a prediction task and a corresponding optimal characteristic combination; in this way, the multi-modal radiomics features, the classifier and prediction tasks of different levels are optimally matched, then preoperative accurate diagnosis of ovarian cancer pathology, staging and accurate prediction of PDS effects are achieved through optimal matching, and the prediction process is close to the clinical practice diagnosis and treatment thought; and iconography detection suggestions with the highest diagnostic value are provided for different diagnosis and treatment layers.

The invention discloses a tumor prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention relates to a UTE-CT cross-modal image style migration method and device based on a cyclic generative adversarial network. The method comprises the following steps: using a U-Net network added with a space channel double attention mechanism module scSE-block as a segmentation network to cut a UTE / CT image; a TransMorph network which is combined with a Transform and a ConvNet is adopted to carry out registration on the UTE / CT image; and carrying out cross-modal style migration on the UTE / CT image by adopting CyclGan. According to the method, the UTE image is converted into the CT-like image, the quality of the UTE image is improved, and the clinical application environment of the UTE is expanded to conventional screening and diagnosis of respiratory system diseases from reexamination and comparison of clearly-diagnosed large focuses. Meanwhile, the CT-like image obtained after migration is more in line with the radiograph reading habit of imaging doctors, multi-angle and multi-mode focus information can be provided for the doctors, and the diagnosis accuracy is further improved. The method is also used for data enhancement to generate diversified and high-quality high-throughput medical images, so that a training data set is enriched, and the generalization ability and performance of a deep learning radiomics model are improved.

The invention discloses an esophageal cancer neoadjuvant chemotherapy and post-immunization pathology complete remission prediction system and method, and relates to the field of medical image processing, and the system comprises an image processing module which is used for carrying out the standardized processing of an input image, and segmenting a tumor region in the image; the feature extraction module is used for extracting voxel-level radiomics features from the tumor area; the feature screening module is used for screening out a plurality of voxel-level radiomics features with the most predictive capability; and the pathology prediction module is used for generating a comprehensive feature vector according to the segmented input image and the screened voxel-level radiomics features, and predicting the curative effect of the combination of neoadjuvant chemotherapy and immunotherapy according to the comprehensive feature vector. According to the technical scheme provided by the invention, the accuracy of predicting complete pathologic remission after neoadjuvant chemotherapy and immunotherapy of esophageal cancer can be improved, the defect that the heterogeneity in tumors cannot be considered in a traditional imaging omics technology is overcome, and the method has a relatively high application value.

The invention relates to the technical field of image analysis, in particular to a multi-modal data fusion IDH wild type glioblastoma typing method and system, and the method comprises the steps: obtaining a data set of a GBM patient; based on the data set, radiomics characteristics, pathomics characteristics, whole exon sequencing data, transcriptome sequencing data and proteomics characteristics are obtained; performing multi-modal data fusion on the image omics characteristics, the pathological omics characteristics, the whole exon sequencing data, the transcriptome sequencing data and the proteomics characteristics, and constructing a multi-modal fusion typing framework to identify the MOFS subtype; and based on image omics features, constructing a deep neural network classifier for predicting the MOFS subtype, and verifying the identified MOFS subtype. Three different subtypes in IDH wild-type adult glioma are determined and verified using an artificial intelligence method by integrating multi-mode data from MRI derived radiomics, WSI derived pathomics, WES, RNA sequencing, and MS-based proteomics.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention relates to a glioma radiotherapy effect prediction method based on habitat analysis imageomics, and belongs to the technical field of medical image artificial intelligence and tumor radiotherapy, and the method comprises the steps: collecting a multi-mode MRI image, a radiotherapy plan computed tomography CT image, a dose image and a local recurrence label before postoperative radiotherapy, and converting the images into a standard format; sequentially performing craniocerebral boning processing, N4 bias field correction, isotropic resampling, gray level normalization processing and image registration on the acquired image to obtain a preprocessed image; based on the planned target volume PTV, dividing the interior of the PTV into three types of physiological structure habitat areas and three types of physiological habitat areas; image omics features of the multi-mode MRI images are extracted in the PTV and all habitat areas, and an optimal feature subset is screened out; taking the screened optimal feature subset as an input, taking the local recurrence label as an output label, and selecting an optimal model; and generating an output result by using the optimal model.

The invention discloses a heart failure combined left beam branch conduction block classification method based on feature fusion, and the method comprises the steps: extracting radiomics features and deep learning features through a heart magnetic resonance image, carrying out the feature fusion and selection, constructing a classification model through a machine learning algorithm, and carrying out the classification of the heart failure combined left beam branch conduction block. According to the method, the advantages of deep learning in the aspect of image processing and the high efficiency of machine learning in a classification task are combined, and complex information of a heart image can be reflected more comprehensively and accurately by combining radiomics features and deep learning features; an automatic and intelligent feature extraction and classification method is utilized, a large amount of medical image data can be rapidly processed and analyzed, accurate classification of the heart failure combined left bundle branch conduction block is achieved, the method has higher classification accuracy and higher robustness, and in the field of medical image classification, the method has the advantages that the classification accuracy is improved, and the classification efficiency is improved. And the efficiency and the quality of medical services can be improved.

The invention discloses a method and a system for visually identifying hemodynamic spatial-temporal heterogeneity in breast tumors, and belongs to the technical field of medical image acquisition and auxiliary detection. The method for visually recognizing the hemodynamic spatial-temporal heterogeneity in the breast tumor comprises the steps that S1, DCE-MRI images of breast benign and malignant tumors are obtained, and region-of-interest sketching is carried out; s2, carrying out TIC automatic classification and coding on each voxel in the tumor by adopting 19 types of TIC curves to obtain a TIC color coding graph; s3, calculating the TIC type and proportion of each focus, and extracting a TIC color coding map to reflect radiomics characteristics of tumor internal heterogeneity; s4, screening the image omics characteristics of the TIC color coding image, and establishing three models including 19 types of TIC curve fusion models; according to the method, the difference of hemodynamics time and space heterogeneity in the breast tumor of the patient can be visually judged and compared, and benign and malignant and biological behaviors of the tumor of the patient are judged.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a meniscus injury prediction method and system based on a neural network, and the method comprises the steps: obtaining a knee MRI image, carrying out the automatic segmentation of the knee MRI image based on a convolutional neural network, extracting a plurality of image features based on the segmented image, and obtaining a meniscus injury prediction result. And constructing a meniscus damage prediction model based on the SNN network model, and carrying out model training and verification. The meniscus injury diagnosis accuracy and diagnosis efficiency are improved, future injury risk prediction is achieved, valuable prediction information is provided for clinicians, prevention and treatment schemes are helped to be formulated, meanwhile, radiomics feature and load structure feature heat maps and a mixed attention mechanism are introduced, the clinical interpretability is enhanced, and the diagnosis accuracy and efficiency of meniscus injury are improved. And a full-automatic diagnosis process is realized.

The invention provides a nerve invasion prediction system and method based on a hybrid model, and electronic equipment. The method comprises the following steps: acquiring initial medical information; preprocessing the initial medical information to obtain target medical information; based on the target medical information, acquiring deep learning image features through multi-scale convolution and an attention mechanism of a deep learning model, investigating a tumor region and a peritumor region through a radiomics model, and acquiring radiomics image features; carrying out feature splicing on the radiomics image features and the deep learning image features to obtain classification model input information; and inputting the classification model input information into the classification network to obtain a category prediction result of the initial medical information. According to the feature extraction method combining deep learning and radiomics, the features of the tumor can be captured from different angles, the accuracy of nerve infringement prediction is improved, and the precision requirement of nerve infringement prediction of the prostate part is met.

The invention discloses a method for constructing a machine learning model based on cerebellar subregion multi-modal radiomics. According to the method, [18F] FDG PET metabolic features and 3DT1 W MRI structural features of a cerebellar subregion are extracted, a random forest classification model is constructed after feature selection, and high-precision identification of the Alzheimer's disease (AD) and a cognitive normal (CN) is realized in combination with SHAP analysis. According to the method, the multi-modal radiomics characteristics of the cerebellar subregion are integrated for the first time, the accuracy and interpretability of AD early diagnosis are improved, and a non-invasive and efficient diagnosis tool is provided for clinic.

The invention discloses a postoperative portal vein pressure prediction system, which relates to the technical field of portal vein pressure prediction, and is characterized in that three-dimensional structure regions of interest of the liver and the spleen are segmented based on preoperative and postoperative abdomen enhanced CT vein phase image data of a patient; image omics features including first-order statistical features, texture features and shape features are extracted from the segmented regions of interest of the three-dimensional structures of the liver and the spleen, and core feature screening is carried out on the extracted features; integrating the screened core features with clinical hemodynamic parameters and surgical parameters to construct a multi-modal prediction model of the portal vein pressure gradient; and predicting and outputting a portal vein pressure gradient predicted value by using the multi-modal prediction model of the portal vein pressure gradient, and determining a postoperative portal vein pressure gradient risk grade of the patient. The portal vein pressure gradient prediction method solves the problems of insufficient dynamic evaluation capability, multi-modal information integration and risk layering application in the prior art, and realizes non-invasive and accurate prediction of the portal vein pressure gradient.

The invention discloses a breast tumor accurate segmentation method based on a space-time fusion network, which adopts the space-time fusion network fusing convolution, LSTM and PK feature fusion to capture space and time features at the same time so as to improve segmentation precision and enhance medical interpretability, and introduces LSTM into each layer of an encoder of the space-time fusion network to capture sequence dependence, so that the segmentation precision is improved. And the model can learn the mode in the whole dynamic enhancement process. In addition, parameters of the classic PK model are fused into network input and training processes, so that the medical interpretability of the model is improved. By comprehensively utilizing a deep learning framework of DCE-MRI spatio-temporal dynamic information and radiomics characteristics, high-precision segmentation of breast tumors is realized, and medically interpretable tumor characteristics are provided, so that doctors can make more accurate diagnosis and treatment plans based on segmentation results.

The invention relates to the technical field of medical risk assessment, in particular to a thyroid cancer risk assessment system and device based on multiple modes. The system comprises a data preprocessing module used for unifying formats and scales of radiomics images, transcriptomics data and clinical medicine texts; the multi-modal feature extraction module is used for extracting image data features by adopting 3D ResNet-34 as a bottom feature extractor and combining a deformable convolution technology; performing feature selection on the exosome miRNA data through a machine learning model, and performing importance sorting on features through an SHAP method; performing feature extraction on the clinical medicine text data through a multi-granularity semantic fusion network architecture; the cross-modal dynamic fusion module is used for aligning and fusing features; and the decision and interpretability module is used for generating a diagnosis result and providing interpretability analysis. According to the technical scheme, the accuracy and efficiency of thyroid cancer risk assessment can be improved.