150 results about "Enhanced ct" patented technology

The invention provides a vascular calcification analysis method and device based on a non-enhanced CT image, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing scanning parameter information of a non-contrast enhanced scanning image set; standardizing the non-contrast enhanced scanning image set based on the scanning parameter information to obtain a preprocessed image sequence; inputting the preprocessed image sequence into a multi-modal registration network, and generating a fused image based on the obtained key anatomical mark points; inputting the fused image into an attention-enhanced segmentation network, and determining blood vessel segmentation masks of the thoracic aorta, the common carotid artery and the intracranial artery; a candidate voxel set is extracted from the fused image based on the blood vessel segmentation mask, three-dimensional connected domain analysis is carried out on the candidate voxel set, and a blood vessel calcification three-dimensional area is determined; and inputting the vascular calcification three-dimensional region into the multi-task prediction model, determining a quantitative score of the vascular calcification degree, and improving the accuracy and reliability of a vascular calcification evaluation result.

The invention discloses a liver enhanced CT (Computed Tomography) feature extraction and fusion method based on a vascular topological structure, which relates to the technical field of medical image processing, and comprises the following steps: acquiring a liver enhanced CT image, preprocessing the liver enhanced CT image, rigidly registering the liver enhanced CT image and then taking the same as input, and acquiring a time feature vector based on a time feature extraction model; partitioning the registered liver enhanced CT image according to a vascular topological structure to obtain a plurality of liver partitioned images; dividing the time feature vector according to the plurality of liver partition images to obtain a partition feature vector corresponding to each liver partition image, taking the partition feature vectors as input, and performing spatial feature extraction and fusion based on a spatial feature extraction model to obtain a time-spatial feature vector; and calculating the time-space feature vector to obtain a classification result, and the like. According to the method, finer time and space feature extraction fusion of the liver enhanced CT image is realized, and the liver cancer detection and classification precision is improved.

The invention provides a clinical diagnosis and treatment path generation method and system for tracking a chest pain patient. Wherein a dynamic spatio-temporal data atlas is constructed by synchronously collecting respiratory rhythm phase difference, coronary artery calcification integral and hypersensitive troponin curve slope, and risk degree transition signals are generated by coupling analysis of ST segment elevation amplitude asymmetric growth, left ventricular ejection fraction attenuation gradient and B-type natriuretic peptide release rate. In the acute stage, the constraint relation between a vascular recanalization time window and thrombolysis taboo is dynamically bound, in the subacute stage, a phase synchronization rule for enhancing CT scanning and anticoagulant drug dosage is established through a multidisciplinary interface, intravascular ultrasonic plaque morphological mutation characteristics and extracorporeal circulation parameters are fused, the blood supply reconstruction time sequence is dynamically adjusted based on a myocardial oxygen consumption balance equation, and the thrombolysis time sequence is determined. And path self-optimization is realized through space-time superposition of an interventional therapy delay parameter and a drug metabolism curve. According to the technical scheme provided by the invention, chest pain diagnosis and treatment path dynamic optimization and multi-modal parameter collaborative decision-making are realized, and the risk layering precision and the treatment timeliness are improved.

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 discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

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 relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

The invention discloses a liver tumor image real-time segmentation method and system based on a YOLO algorithm, and the method comprises the steps: obtaining CT images and MRI images, and calculating the contrast indexes and signal-to-noise ratio indexes of a plurality of CT images; preprocessing the CT image, and dynamically adjusting an image enhancement strategy; a residual attention module is added on the basis of the YOLOv8 network, a multi-scale mask branch is introduced, and an optimized YOLO-Med segmentation network is constructed; inputting the enhanced CT image into a segmentation network, and training the segmentation network in combination with a loss function; and when an MRI image is input, through a multi-modal feature fusion mechanism, features of the MRI image and the enhanced CT image are aligned and fused and then are input into the segmentation network, and the segmentation network outputs a pixel-level segmentation mask for real-time segmentation of the liver and the tumor. According to the invention, by fusing the density characteristic of CT and the soft tissue resolution capability of MRI, the small tumor (diameter lt; 5 mm).

The invention provides an evaluation method for gastric cancer peritoneal metastasis state recognition and PCI score estimation, and the method comprises the steps: receiving a preoperative abdominal enhancement CT image, and carrying out the automatic positioning preprocessing of the peritoneum through resampling, normalization, image enhancement and a region attention mechanism; image omics features are extracted from the region of interest and fused with the depth features of the multi-scale convolutional neural network, and a binary classification model based on a residual network / Transform is constructed to output transition state probability and confidence; pCI scores of all the areas are quantitatively evaluated synchronously through the peritoneal thirteen subareas, and finally a transfer prediction result, a PCI spatial distribution map, a visual heat map and a model interpretation report are integrated to form structured diagnosis output. According to the method, transition state identification and PCI score quantification can be realized, and the problems of single function, weak generalization, low interpretability and the like of a traditional model are solved.

The invention discloses a CT image change-based lung cancer curative effect dynamic prediction method and system, and relates to the technical field of medical imaging. Clinical information, a baseline CT image and a follow-up CT image of a patient are acquired; performing feature extraction on the baseline CT image and the follow-up CT image to obtain baseline features and follow-up features; obtaining an enhanced CT dynamic feature matrix and a flat-scanning CT dynamic feature matrix based on the baseline features and the follow-up visit features; inputting the enhanced CT dynamic feature matrix, the plain-scan CT dynamic feature matrix and clinical information into a curative effect prediction model, outputting logic risk scores at different time points, and obtaining progress or death risk probabilities of the patient at different time points; according to the method, image feature changes in a baseline and follow-up visit are effectively represented through a dynamic feature matrix, correlation between the interior of the dynamic feature matrix and between different matrixes is identified through a Transform attention network to predict the curative effect, and the relationship between accumulated data at each follow-up visit time point and the curative effect is learned through a time-sharing training strategy.

The invention discloses a rectal cancer tumor T stage differentiation labeling method based on an enhanced CT image, and relates to the field of medical image data processing, and the method comprises the following steps: a standardized manual labeling module which is used for constructing three-dimensional pixel-level labeling data of a rectal cancer tumor T stage according to a medical image and a pathological stage standard; the data set construction module is used for uniformly storing and organizing the original CT image, the annotation mask file and the matched label description document to form a structured data set which can be used for modeling; according to the rectal cancer tumor T-stage differentiation labeling method based on the enhanced CT image, a high-quality and standardized three-dimensional T-stage labeling data set is constructed, a three-dimensional pixel-level stage labeling process for the rectal cancer enhanced CT image is provided based on the AJCC eighth version tumor stage standard, the labeling content covers tumor focuses and normal intestinal wall structures around the tumor focuses, and the three-dimensional T-stage labeling data set is established. A plurality of high-annuity imaging department doctors perform independent blind marking, expert re-checking, quality rating and the like.

The invention provides an interactive recognition measurement method and device based on an AI non-enhanced CT image, and relates to the technical field of medical image processing. The method comprises the following steps: establishing an image task queue based on a batch liver and spleen CT image set; inputting each liver and spleen CT image in the batch liver and spleen CT image set into a pre-trained organization structure recognition model in sequence according to the first task queue, and determining a segmentation mask corresponding to a target organization structure region; interaction data is received to generate a calibration mask, the model is optimized by combining the segmentation mask and the calibration mask, and the optimized model is used for processing the remaining liver and spleen CT images in the second task queue until recognition of all the liver and spleen CT images is completed; and determining a CT value measurement result of each liver and spleen CT image through a pixel gray value in the mask region. According to the scheme, the tissue structure recognition precision and the CT value measurement stability and accuracy of batch liver and spleen CT images during recognition and measurement can be improved.

The invention relates to an automatic blood vessel segmentation method and system for a CT image. The method comprises the following steps: acquiring an enhanced CT image and a plain-scan CT image at the same position; inputting the enhanced CT image to a pre-trained first blood vessel automatic segmentation model to obtain a first blood vessel segmentation result; and registering the enhanced CT image and the plain-scan CT image to mark and map the first blood vessel segmentation result to the plain-scan CT image so as to obtain a second blood vessel segmentation result. The method has the advantages that a deep learning model (such as a registration network based on U-Net or Transform) is utilized to calculate a deformation field, and in combination with multi-scale feature enhancement and regularization strategies, the precision and stability of enhanced CT and plain-scan CT image registration are improved, and artifacts and local distortion in the deformation field are reduced; in order to solve the problem that the contrast difference of a plain scanning CT image and an enhanced CT image is significant, a compensation strategy of multi-modal texture and intensity distribution is introduced, such as adversarial loss and structural similarity index (SSIM) optimization, so that registration is more robust among different modals.

The present invention provides a method and system for automatically segmenting blood vessels within organs in enhanced CT images. The method includes performing organ segmentation processing on the enhanced CT images to extract the organ regions, and obtaining three-dimensional regions of interest in the organs from the organ regions; within the three-dimensional regions of interest in the organs, performing global blood vessel segmentation within the organs and local blood vessel segmentation within the organs respectively to generate a global automatic blood vessel segmentation result within the organs and a local automatic blood vessel segmentation result within the organs; and fusing the global automatic blood vessel segmentation result within the organs and the local automatic blood vessel segmentation result within the organs to obtain the final automatic blood vessel segmentation result within the organs. The present invention proposes a fully automatic progressive deep learning solution for segmenting blood vessels within organs, aiming to maintain the integrity of blood vessels in terms of their shape details and continuity. Experimental results show that this solution has high accuracy, efficiency, and robustness.

The invention discloses an image recognition and enhanced CT image generation method based on a plain-scan CT image. The method comprises the following steps: acquiring an NCE-CT image and CE-CT image pairing data set; calculating hemodynamic parameters of the aortic true cavity by using CFD, and obtaining physical prior features through feature selection; constructing and training a PIAD deep learning model; the PIAD deep learning model comprises an encoder guided by physical prior features through a cross attention mechanism, a Transform global information extraction module, and three functional heads for respectively outputting a classification result, a CE-CT image and a segmentation mask; training a PIAD deep learning model; and applying the trained PIAD deep learning model to a reasoning stage of only inputting an NCE-CT image, and synchronously obtaining a corresponding CE-CT image, a true cavity and false cavity segmentation result and an aortic dissection recognition result. According to the method, physical prior information is integrated, and a real cavity and false cavity segmentation task and a CE-CT generation task are combined, so that complementary information of NCE-CT and CE-CT is effectively utilized, and dependence on expensive and complex CE-CT equipment is reduced.

The invention relates to the technical field of medical image processing, in particular to a liver cancer intervention auxiliary system. According to the method, the boundary structure and curvature change of the liver region can be extracted from multi-source images such as MRI, CT and enhanced CT through cooperative processing of multi-modal images, and candidate path segments are constructed step by step by utilizing continuity and direction stability of a boundary turning region; sorting identification is carried out on the candidate path segments in combination with image parameters such as gray distribution, texture features and background contrast, path segment quality levels based on multiple image characteristics are established, and path areas with multi-modal parameter advantages in the image are enhanced and displayed by evaluating response distribution of the sorting levels and carrying out weight adjustment, so that the image quality is improved. And finally, the path information after transparency adjustment or edge enhancement is superposed in the original image frame, so that the image presents a clearer path structure and spatial distribution, and the visual perception capability of a doctor on the direction of the intervention path is improved.

The invention discloses a collaborative optimization multi-task non-enhanced CT blood vessel segmentation device. The collaborative optimization multi-task non-enhanced CT blood vessel segmentation device comprises an image acquisition module, an image preprocessing module, a network construction module, a multi-task multi-scale joint loss function construction module, a branch loss construction module, a joint training module and a branch training module. According to the method, collaborative optimization of double tasks can be promoted while the parameter quantity is greatly reduced in an encoder sharing mode, branch parameters of the tasks are independently and finely adjusted, so that the branch parameters of the tasks are optimal, and the blood vessel form mask and the blood vessel structure mask can be effectively segmented from the non-enhanced CT image.

The invention relates to a tumor and blood vessel three-dimensional space relation quantitative analysis method based on an enhanced CT image, and the method comprises the following steps: obtaining a thin-layer enhanced CT image, and carrying out the segmentation processing of the thin-layer enhanced CT image, and obtaining a tumor and blood vessel segmentation result; extracting a blood vessel center line from the segmentation result to generate a local plane, and reconstructing to obtain two-dimensional slices corresponding to each point on the blood vessel center line, including a blood vessel slice and a tumor slice; and based on the blood vessel section and the tumor section, determining an interaction area and calculating a wrapping angle. Compared with the prior art, the method has the advantages that the three-dimensional space relation between the tumor and the main blood vessel can be efficiently and accurately quantified, especially the wrapping angle can be calculated, and therefore more accurate and quantitative data can be provided for follow-up operation resection evaluation.

The invention provides a kidney cancer recurrence risk prediction method based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: collecting an image data set for kidney cancer high recurrence risk prediction; carrying out registration on the collected multi-stage enhanced CT image; constructing and training a kidney tumor automatic detection and segmentation model; carrying out ROI positioning cutting and quality control; and constructing a deep learning model for renal cancer recurrence risk prediction based on the multi-modal convolutional neural network, and realizing renal cancer recurrence risk prediction through the constructed prediction network model. According to the method, the multi-phase enhanced CT image of the kidney cancer patient is analyzed through the deep learning model, the tumor postoperative recurrence risk is predicted, an objective basis is provided for a clinician to make an individualized follow-up visit scheme and an auxiliary treatment decision, and excessive treatment of a low-risk patient and insufficient treatment of a high-risk patient are avoided.

The invention discloses a liver tumor diagnosis method based on a self-supervision and multi-phase contrast enhanced CT image. The method comprises the following steps: collecting and preprocessing a CT image of a liver tumor; training a target detection model by using the marked CT image; processing the preprocessed CT image by using the trained target detection model, obtaining a focus detection frame, performing cutting and data enhancement according to the focus detection frame, performing resampling, adding a real joint label to the focus detection frame, and constructing a second training set and a test set; constructing a classification diagnosis model based on multiphase data based on self-supervision and knowledge distillation, performing iterative training by using the second training set, and adjusting parameters according to a total loss function of the classification diagnosis model in the training process to obtain a final liver tumor classification diagnosis model; and inputting the CT image in the test set into the final liver tumor classification diagnosis model to obtain a diagnosis result. The precision, accuracy and efficiency of liver tumor diagnosis can be improved.

The invention discloses an enhanced CT (Computed Tomography) image generation method and equipment based on double-space constraint, which are characterized in that a real enhanced CT image is coded to obtain parameterized distribution of the real enhanced CT image in a hidden space, and the parameterized distribution serves as a label in the hidden space to provide extra training supervision information to optimize model parameters; a real segmented binary image of an enhanced area is combined to explicitly guide an area which the model should focus on, and the attention degree of the model on a fine anatomical structure in a plain-scan CT image is improved by improving the weight of the real segmented binary image area in reconstruction loss, so that the model can enhance the fine anatomical structure; according to the conversion encoder, a channel-space attention module is added to serve as the conversion encoder on the basis of an encoder of a VAE model, so that mapping from an image space of a plain-scan CT image to a real enhanced CT image hidden space is achieved.

The invention provides a navigation method and system for percutaneous transhepatic duct puncture, and the method specifically comprises the steps: S1, reading a liver enhanced CT image, and determining a target bile duct to be punctured; s2, integrating the electromagnetic tracking navigation system and the ultrasonic system to form an ultrasonic-electromagnetic fusion workstation; s3, electromagnetic sensors are installed in the ultrasonic probe and the puncture needle respectively; s4, starting a magnetic field generator to obtain object space vector information; s5, acquiring an ultrasonic medical image, and generating a positioning image according to the object space vector information; s6, setting the positioning image and the ultrasonic medical image according to the same space size proportion; s7, obtaining a superimposed image, and outputting the superimposed image on a display monitor in real time; s8, simulating puncture, offsetting magnetic field interference through parameter adjustment, and adjusting puncture precision; s9, puncturing along the puncturing path; and the like. The percutaneous transhepatic duct puncture navigation system can quickly and accurately complete percutaneous transhepatic duct puncture navigation, is flexible and free of radiation, is short in learning curve, and can be popularized in a homogenized manner.

The invention belongs to the technical field of medical auxiliary instruments. In order to solve the problems that when an existing device is used, the comfort degree is poor, and discomfort caused by the fact that the upper limbs of a patient are kept still for a long time cannot be relieved, the arm fixing device for radiotherapy positioning enhanced CT scanning comprises a mounting frame, a large arm fixing mechanism, a small arm fixing mechanism and a relieving mechanism; wherein the big arm fixing mechanism is respectively connected with the mounting frame and the small arm fixing mechanism, and the relieving mechanism is arranged on the small arm fixing mechanism; the height of the big arm fixing mechanism and the forearm fixing mechanism can be adjusted so as to be suitable for patients with different figures, and the relieving mechanism can drive the fingers of the patients to slightly move so as to avoid discomfort caused by the fact that the upper limbs of the patients keep still for a long time.

The utility model belongs to the field of films, particularly relates to a special film for enhancing a CT (Computed Tomography) indwelling needle, and aims to solve the problems that the conventional film cannot improve the stability and comfort of the film on the skin of a patient, so that the film is loosened and falls off or the patient feels uncomfortable, and the comfort and safety are reduced. A hollow hole matched with the shape of an indwelling needle is formed in the center of the top of the film body, and the film body comprises a protective layer, a breathable layer and an adhesive layer; the fixing assembly is arranged on the membrane body and used for reinforcing fixation of the membrane body. Through the combination of the fixing assembly, the hollow holes and the film body, the stability of the sticking film on the skin of a patient is remarkably improved, the sticking film is effectively prevented from loosening or falling off, safety and stability of an indwelling needle in the examination process are ensured, meanwhile, discomfort of the patient is relieved, and comfort and safety are improved.

The application belongs to the technical field of liver images, and provides a liver image synthesis method for generating three-phase enhanced images based on plain CT, S1, an upper abdominal plain CT image of a patient to be processed is acquired; S2, a pre-trained liver mask guided corrector module is used to respectively perform affine transformation and dense deformation registration on real arterial phase, portal phase and delayed phase enhanced CT images matched with the plain CT image, to obtain registered enhanced images and corresponding liver masks in the liver region which are aligned with the plain CT image at the voxel level; S3, the plain CT image, a 2.5D input tensor formed by the plain CT image and its adjacent upper and lower slices, and the current layer plain image as a residual base are input into a pre-trained Liver-GAN generator, and an attention mechanism based on the liver mask is used to weight the features, and a predicted enhanced residual is output; S4, the enhanced residual and the residual base are added to generate a virtual arterial phase, portal phase or delayed phase enhanced image corresponding to the plain CT image.

The present application provides a method, device, electronic device, and storage medium for subtracting coronary CT images, including: obtaining an enhanced CT image and a plain CT image of a target patient; extracting a first feature point set of the enhanced CT image using a first feature point extraction model; extracting a second feature point set of the plain CT image using a second feature point extraction model; determining a target transformation matrix based on the first feature point set and the second feature point set; performing coordinate transformation processing based on the plain CT image and the target transformation matrix to obtain a transformed plain CT image; and obtaining a subtracted CT image by subtracting the grayscale values ​​of the corresponding pixels in the transformed plain CT image from the grayscale values ​​of all pixels in the enhanced CT image. The subtraction method provided by this solution can automatically obtain a subtracted CT image, and by accurately registering the enhanced CT image and the plain CT image, the accuracy of the subtracted CT image is improved.

The application discloses a classification method and device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: segmenting tumor masks from enhanced CT images; for each tumor mask, generating a first peritumoral region and a second peritumoral region; dividing the tumor into multiple habitats for each tumor mask; extracting 3D radiomics features, multiple peritumoral microenvironment features, multiple tumor habitat features, multiple local pathological features and multiple global context features; selecting multiple target features for each enhanced CT image and calculating a radiomics score; inputting the radiomics score and clinical variables into a logistic regression model for training to obtain a trained logistic regression model; and using the trained logistic regression model to classify the enhanced CT images to be classified. The application can quickly and accurately realize G-grade classification of pancreatic neuroendocrine tumors, and provides a reliable non-invasive evaluation tool for clinical decision-making.

The present invention provides a method for identifying pulmonary embolism based on non-contrast CT, belonging to the technical field of deep learning. In order to solve the problem in the prior art that the identification of pulmonary embolism mostly relies on contrast-enhanced CT and it is impossible to quickly and accurately identify pulmonary embolism for non-contrast CT data. The method of the present invention performs pulmonary contour segmentation on non-contrast CT images through a pulmonary contour segmentation model; performs segmentation of pulmonary blood vessels in non-contrast CT through a blood vessel segmentation model; through a pulmonary embolism extraction model, uses the pulmonary embolism area on contrast-enhanced CT as the gold standard to mark the pulmonary embolism area in non-contrast CT, determines the embolism suspicious area by considering the CT value difference between pulmonary embolism and blood vessels and the vascular morphological features caused by pulmonary embolism for the pulmonary embolism in non-contrast CT, optimizes the model by minimizing the difference between the suspicious area and the gold standard, and uses the optimized model to perform pulmonary embolism segmentation on non-contrast CT. The present invention can achieve high sensitivity and accuracy in predicting pulmonary embolism based on non-contrast CT.

A computer-implemented method of enhancing visibility of a contrast agent in computed tomography (CT) imaging is provided. The method comprises receiving CT data representing a CT image (110) comprising the contrast agent; generating, from the CT data, one or more corresponding contrast agent enhanced CT images (130), wherein the visibility of the contrast agent is enhanced as compared to the CT image (110) using a trained machine learning model (120); and outputting the one or more contrast agent enhanced CT images (130). A contrast agent dose used in the received CT data is lower than a contrast agent dose in a corresponding conventional CT image comprising the contrast agent.

The application provides a breast cancer patient lymph node metastasis prediction model without clinical pathological characteristics, comprising the following steps: after three-dimensional reconstruction of two-dimensional lung enhanced CT films, an axillary lymph node atlas is established, all axillary lymph nodes in the atlas are selected as ROI regions, and more than 5 combined image features of each axillary lymph node are selected to distinguish whether breast cancer has axillary lymph node metastasis; and a logistic regression machine learning prediction model is used to construct the breast cancer patient axillary lymph node metastasis prediction model. The model established by the application can non-invasively predict whether breast cancer has axillary lymph node metastasis, the clinical pathological characteristics of the patient are not included in the model, and the image cutting in the model is not based on breast tumors, but based on axillary lymph nodes; the model is used to determine a suitable axillary treatment scheme, thereby avoiding unnecessary axillary surgery and complications, and helping to carry out more accurate surgery and adjuvant therapy mode of breast cancer.