142 results about "Chest ct" 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.

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 relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention relates to the technical field of medical data analysis, in particular to a sepsis prediction system based on deep learning. The system comprises a sepsis risk assessment module, a capillary barrier injury analysis module, a myocardial inhibition analysis module and a sepsis prediction module, and blood lactic acid concentration data of a patient are obtained through a lactic acid analyzer; acquiring oxygenation state data of a patient through a blood gas analyzer, and performing sepsis risk assessment according to the oxygenation state data of the patient and the blood lactic acid concentration data of the patient, so as to obtain sepsis risk data; obtaining a lung structure image through a high-resolution chest CT; performing alveolar feature extraction according to the lung structure image to obtain alveolar data; according to the pulmonary alveolar data, capillary barrier injury analysis is carried out, so that capillary barrier injury data is obtained; an echocardiogram is obtained by an echocardiograph. The sepsis prediction accuracy is improved based on a medical data analysis technology.

The invention discloses an intelligent chest CT image processing system which comprises an image acquisition module, a multi-scale filtering processing module, a frequency domain edge enhancement module, an edge fusion correction module, an enhancement loss design module, a chest CT image segmentation model establishment module and a chest CT image processing module. The invention belongs to the field of image processing, and particularly relates to an intelligent chest CT (computed tomography) image processing system, which is characterized in that differentiated processing is performed in different frequency spectrum sections according to noise characteristics, key points are guided to be reserved, and blood vessel walking is enhanced; through edge fusion correction, adaptive reinforcement of small-range nodules and large-range lesions is considered; the branch A is used for refining the nodule edge and artifact weak contrast, and the branch B is used for strengthening the lung lobe macroscopic consistency; the decoding layer inhibits a learned region, focuses a blood vessel and a lung parenchyma junction through interface focusing; the loss is introduced into boundary smoothing, the overall segmentation contour is optimized, and false positive burr-shaped noise is reduced; and the chest CT image processing accuracy is improved.

The invention discloses a three-dimensional pulmonary nodule detection method and system based on morphological adaptation convolution, and the method comprises the steps: obtaining a chest CT image sequence, and carrying out the preprocessing of all chest CT images in the chest CT image sequence; according to the three-dimensional pulmonary nodule detection method and system based on form adaptive convolution, scale factors are introduced into a 3D dynamic convolution layer and cooperate with the offset, so that the position of a convolution kernel sampling point can be adaptively adjusted according to the form and size of the pulmonary nodule, and the problem that the traditional fixed convolution is difficult to adapt to the size difference and form heterogeneity of the pulmonary nodule is solved; stable detection of pulmonary nodules with different sizes, especially tiny pulmonary nodules, is realized; space coordinate information and multi-scale features are combined through a channel space attention module, extraction of key information such as pulmonary nodule edge texture and density gradient is enhanced, interference of blood vessel, trachea and CT artifacts is inhibited, and the problem that small nodules and background noise are difficult to distinguish is solved.

The invention discloses a pulmonary nodule image segmentation method and device based on optimization down-sampling and feature fusion, and relates to the technical field of image processing, the method comprises the steps: obtaining a data set containing a plurality of chest CT images, cutting the chest CT images in the data set into a preset-size region of interest containing pulmonary nodules, and obtaining a plurality of pulmonary nodules; constructing to obtain a training set, a verification set and a test set; the method comprises the following steps: constructing an SPDD module for realizing space-to-depth downsampling and a DPFM module for realizing dual-branch fusion, and creating a DFNet model based on the constructed SPDD module and DPFM module; and training, verifying and testing the DFNet model based on the training set, the verification set and the test set, and evaluating the performance of the DFNet model. The pulmonary nodule segmentation capability of the model can be effectively improved.

The invention belongs to the field of medical image analysis and artificial intelligence, and provides a COPD early screening method and application based on a dynamic dependency graph and self-supervised learning for the problems of insufficient sensitivity, insufficient feature representation and the like in the prior art, and the method comprises the steps: S1, preprocessing chest CT image data, and obtaining an enhanced image block; s2, generating an image block sequence of the enhanced image blocks; s3, inputting the image block sequence into a self-supervised learning Transformer encoder, and constructing a total loss function of self-supervised learning; and S4, determining a model for predicting the COPD risk probability, and constructing a loss function with a supervised classification task to optimize the model of the COPD risk probability. And S5, constructing a comprehensive loss function, and integrally optimizing the model for predicting the COPD risk probability. According to the method, multiple advanced technologies are fused, and early detection and risk assessment are carried out on COPD in an automatic, efficient and high-precision mode.

The invention relates to the field of electrocardiogram treatment, and discloses an individualized electrocardiogram operation process relying on iconography examination and an AI large model, which comprises the following steps: S1, chest CT image acquisition: a CT scanner is used for acquiring chest image data of a patient, the CT scanner can generate high-resolution multilayer CT images, and the data format comprises but is not limited to a DICOM format; s2, heart projection position analysis: carrying out image preprocessing, heart segmentation, heart center point calculation and heart edge positioning on the acquired CT image data; and S3, lead position optimization: generating a lead electrode position based on a heart projection position by using an AI large model. According to the method, the actual position and form of the heart of each patient in the thoracic cavity can be accurately determined through CT image acquisition and heart projection position analysis, and the optimized lead position is generated by utilizing the AI large model based on the individualized projection positions, so that the lead electrode can more accurately capture the electrical activity of the heart.

The invention provides a calcification section coronary artery risk prediction method and device based on chest CT (Computed Tomography) plain scanning. The method comprises the following steps: firstly, acquiring and screening chest CT (Computed Tomography) plain-scan image data; secondly, performing calcification section coronary artery labeling on the image data; thirdly, performing CCTA image interpretation and grouping on the marked areas; and finally, collecting a CT image and a classification label model corresponding to the CT image, using a ResTransNet network to extract features from the calcification section coronary artery label, training the ResTransNet model, and predicting a risk state. According to the method, the deep learning method is applied to the coronary artery of the calcification section of chest CT flat scanning, the plaque type and the blood vessel stenosis degree are accurately predicted, and effective information is provided for subclinical coronary atherosclerosis risk screening and prevention; constructing plaque types and coronary artery stenosis degrees gt; and the prediction device has great clinical significance when being used for screening the risk of subclinical coronary atherosclerosis.

The invention provides an osteoporosis and osteoporosis centrum compression fracture identification model, a training method and a prediction method, and relates to a technical scheme for predicting and identifying osteoporosis and osteoporosis centrum compression fracture by repeatedly utilizing chest CT (Computed Tomography) scanning images of a patient for pulmonary nodule screening based on a deep learning technology. The first input data of the model comprises 2D image information data cut from a chest CT image slice and 2D segmented mask information data cut from a vertebral mask slice; the second input data comprises clinical information of age and gender of the patient; the multi-layer feature extraction layer of the model is used for processing first input data, and a final feature extraction graph is obtained after the first input data passes through the multi-layer feature extraction layer; and the prediction layer is used for splicing the processed final feature extraction image and second input data, and finally performing osteoporosis category prediction and vertebral compression fracture category prediction through the two parallel full-connection layers.

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 relates to the technical field of intelligent medical treatment, and discloses a connective tissue disease related lung disease early screening method and system based on deep learning, and the method comprises the steps: obtaining clinical data, a high-resolution chest CT image and biomarker data of a to-be-screened target; the clinical data, the high-resolution chest CT image and the biomarker data are input into a deep learning feature extraction and fusion module, high-dimensional clinical features, image feature vectors and biomarker time sequence feature vectors are extracted and fused, and fused feature vectors are obtained; and inputting the fusion feature vector into an early screening and typing module, and outputting a lung abnormal risk detection result. According to the application, the effects of early diagnosis of connective tissue disease related lung diseases and timely intervention can be achieved.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention discloses a pulmonary nodule growth prediction method based on the combination of LSTM and GAN, and relates to the technical field of image analysis and processing, and the method comprises the following steps: collecting multi-time-sequence chest CT data, and carrying out the marking and auditing to form a data set; preprocessing the marked CT data to obtain a standardized pulmonary nodule ROI (Region of Interest); segmenting a pulmonary nodule region by using the three-dimensional image segmentation network, and generating a three-dimensional segmentation mask; inputting the segmentation masks of the multiple time nodes into an LSTM network, and extracting a time sequence feature vector representing nodule dynamic evolution; based on the time sequence feature vector and the random noise, the generator synthesizes the predicted pulmonary nodule image at the future moment, and the discriminator jointly optimizes model parameters through multiple loss functions; and inputting the three-dimensional segmentation masks at the current moment and the historical moment, and generating a predicted pulmonary nodule image at the future moment. The method can effectively improve the situation that the prior art is insufficient in utilization of time sequence information and lacks high-quality generation and inference ability.

The invention discloses a pulmonary interstitial image analysis method and system based on clinical prior guidance feature fusion, and the method comprises the steps: obtaining chest CT images of a user at a plurality of time points and corresponding clinical data, and carrying out the data preprocessing and region segmentation; performing feature extraction on the automatically segmented chest CT image and the corresponding clinical data by using specific indexes; specific time points are coded into time embedding vectors, total image feature vectors are projected to the same dimension as the time embedding vectors through a linear layer, and time coding fusion is generated; the most relevant CT image follow-up visit time points are actively'inquired 'and'weighted' by using clinical risk factors, time sequence image feature fusion is carried out, and fusion features after weighted fusion are output; and carrying out progress probability calculation by using the fusion features, predicting the progress risk in the next one year according to the calculation result, and realizing dynamic time sequence feature selection driven by clinical priori knowledge.

The invention discloses a multi-modal feature fusion preoperative lung adenocarcinoma wettability identification and risk assessment method, which can be applied to preoperative typing judgment and risk stratification of early lung adenocarcinoma patients. According to the multi-modal feature fusion preoperative lung adenocarcinoma wettability identification and risk assessment method provided by the invention, a lung adenocarcinoma wettability diagnosis model and a wettability lung adenocarcinoma risk assessment model are constructed through a multi-modal joint modeling method fusing a 3D chest CT image, a 2D key slice image and a radiation text report; according to the method, lung adenocarcinoma wettability diagnosis and risk level prediction of the wettability lung adenocarcinoma are respectively carried out, through the synergistic effect of multi-modal complementary enhancement and an attention mechanism, the information of each data source is fully utilized, and the accuracy and stability of lung adenocarcinoma wettability identification and risk assessment are remarkably improved; the technical problem that in the prior art, accurate identification and risk grading of the lung adenocarcinoma infiltration degree are difficult to achieve through a non-invasive means before an operation is effectively solved.

The invention relates to the technical field of artificial intelligence and medical image analysis, and discloses a cross-time-phase CT image-based classification method for CIP pneumonia, and the method comprises the steps: obtaining the CT image data of the chest of a CIP patient before and after ICIs treatment, carrying out the preprocessing of the CT image data, and inputting the preprocessed CT image data into an offline trained feature extraction IP-VIT network. The feature extraction IP-VIT network comprises an image division module, a dynamic deformable convolution module, a time phase embedding layer, a hierarchical structure of a CP-SwinFormer structure and a slice merging module, a multi-layer dual-time phase feature prompt fusion module and a multi-layer perceptron classifier. According to the feature extraction IP-VIT network based on the cross-time-phase CT image, the relevance between different time-phase CT images can be learned and utilized, so that the accuracy of CT image classification is more comprehensively improved.

The invention provides a construction method of an IPA recognition model, an IPA recognition method, terminal equipment and a storage medium. The construction method comprises the following steps: constructing a training sample set by collected clinical metadata and chest CT image samples; inputting to a pre-constructed initial neural network model for iterative training; wherein the feature extraction layer outputs a focus area prediction result of a chest CT image sample and corresponding image features based on a preset segmentation network, and extracts clinical features; the feature fusion layer fuses the received features and outputs primary fusion features; and the diagnostic reasoning layer maps the weighted primary fusion features into an IPA disease prediction probability based on a full-connection layer network, the IPA disease prediction probability is used as the output of an initial neural network model, training is ended when the segmentation loss function and the preset constraint term both reach preset values, and an IPA recognition model is obtained. According to the method, the timeliness and the accuracy of identifying the invasive pulmonary aspergillus disease can be improved.

The invention discloses a pulmonary artery CT angiography image generation method and device, equipment and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a chest CT plain scanning image of a target object; inputting the chest CT plain scanning image of the target object into the image generation model to obtain a pulmonary artery CT angiography image of the target object; wherein the image generation model is constructed based on a deep learning method, the pulmonary artery CT angiography image is generated by adopting the chest CT plain scanning image, an iodine-containing contrast agent does not need to be used, the cost is low, safety is achieved, and the application range is wide.

The invention provides a chest CT image automatic segmentation and interstitial pneumonia prediction method and system, and the method and system achieve the automatic segmentation of a CT image through deep learning, greatly improve the image analysis efficiency, reduce the error of manual intervention, and reduce the judgment deviation caused by subjective factors. Quantitative features are combined with a statistical model, small changes of lung images are effectively captured, and the accuracy of IP risk prediction is remarkably improved. According to test data, in a pneumonia patient sample, the accuracy rate of model prediction IP is improved to 85% or above, the method provides timely IP risk prompts for clinicians based on an early warning function of CT image segmentation and feature analysis, and is beneficial to optimizing a treatment scheme, delaying or reducing the probability of occurrence of serious complications, and improving the accuracy rate of the model prediction IP in the pneumonia patient sample. According to the automatic image analysis and prediction system, the working pressure of image doctors can be relieved, the film reading burden is greatly reduced, and the response efficiency of a medical system is improved.

The present invention relates to a thoracoscopy simulation apparatus and method for performing a simulation that includes generating a 3D atelectasis model on the basis of a CT lung image and displaying the location of a pulmonary nodule and a safe resection margin. The thoracoscopy simulation method according to the present embodiment is a thoracoscopy simulation method in which at least a portion of each step is performed by a processor, and may comprise the steps of: generating a 3D lung model in which a pulmonary nodule is displayed, the 3D lung model being generated on the basis of a chest CT image of a patient in an inspiratory state; changing the 3D lung model to generate a 3D atelectasis model in an expiratory state; generating a 3D thorax model using the 3D atelectasis model and the location of the ribs included in the chest CT image; and positioning the 3D thorax model in a virtual space and generating a simulation image on the basis of the 3D thorax model and the tracked locations of a thoracoscope and a surgical tool.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

The present invention relates to the field of biometric recognition technology, and specifically to a minimally invasive thoracoscopic surgery guidance assistance system for mediastinal tumors. It includes an acquisition module for acquiring electronic medical record information and acquiring chest CT images and ultrasonic waveforms in real time through an ultrasonic detector; a medical record analysis module for determining the complexity of the medical record based on keywords in the electronic medical record information; a CT analysis module for determining the degree of movement and deflection of the thoracoscope's viewing angle at the current moment; a waveform analysis module for determining the similarity between the ultrasonic waveform and the preset structural waveforms of different structures, and then determining the structural expression; a reminder module for determining the sensitivity of the viewing angle of the thoracoscope at the current moment based on the complexity of the medical record, the degree of movement and deflection, and the structural expression, and performing surgical operation assistance reminders based on the sensitivity of the viewing angle. The present invention can effectively improve the reliability of surgical assistance.

The invention relates to the technical field of image processing, in particular to a medical foreign matter detection method based on image enhancement processing, which comprises the following steps: acquiring a chest CT image, sliding on each pixel point on an original image through a sliding window, and obtaining an adjustment coefficient of each pixel point according to the gradient change of the pixel points in the sliding window; the sliding window slides in different directions of the target pixel point, and the similarity between the windows is obtained according to the gradient direction difference between the target window and the sliding window; further obtaining a motion blur direction; obtaining a correction adjustment coefficient according to the difference between the sliding window and the target window in the motion blur direction; adjusting kernel parameters according to the correction adjustment coefficient; therefore, the effect is better when the Lanczos interpolation algorithm is used for performing super-resolution reconstruction on the original image, the image enhancement effect is improved, and subsequent detection of foreign matters in the CT image is facilitated.

Proposed is an AI-based apparatus for diagnosing pulmonary nodules from a chest CT image. The apparatus includes a backbone module, a pulmonary nodule detection module, and a pulmonary nodule segmentation header, wherein the backbone module includes a convolution module composed of convolutional layers that receive the chest CT image and each generate a convolutional feature map, and a ViT-based ViT module composed of ViT layers, each of which generates a ViT feature map by receiving the convolutional feature map generated in the last convolutional layer among the convolutional layers, the pulmonary nodule detection module calculates coordinates of a suspicious pulmonary nodule area using the ViT feature map generated in the last ViT layer among the ViT layers, and calculates per layer classification probabilities for the respective ViT layers using the suspicious area coordinates, and the pulmonary nodule segmentation header generates a synthesized feature map.

The invention discloses a method for reconstructing chest CT based on an orthogonal biplane X-ray film. The method comprises the following steps of: 1, after preprocessing CT data, generating an orthogonal biplane X-ray film by using a digital reconstruction radiographic image technology, and dividing a paired data set; 2, constructing a reconstruction model for reconstructing the chest CT, wherein the reconstruction model comprises a generator and a discriminator; 3, inputting the training set obtained in the step 1 into a model, generating a CT image, inputting the CT image and a real image into a discriminator, and optimizing the model through a loss function until the generated CT image is difficult to distinguish by the discriminator; and 4, inputting the orthogonal biplane X-ray film of the patient into the trained model to generate a corresponding reconstructed CT image. According to the method, a feature enhancement block is added in a generator coding stage to improve the anatomical feature extraction capability, and meanwhile, a perceptual consistency loss function is designed, so that a reconstructed image is closer to a real image in brightness, contrast and structure, and clinical availability is improved.

The present invention relates to the technical field of data analysis, and in particular to an analysis system for lung disease rehabilitation data, comprising: a preprocessing layer, an analysis layer and a determination layer; a user's lung CT image is uploaded through the preprocessing layer, the preprocessing layer synchronously performs pixel-level segmentation and lesion position marking on the user's lung CT image, and stores the user's lung CT image that has completed the segmentation and marking operations, the analysis layer synchronously receives the user's lung CT image stored in the preprocessing layer, and based on the pixel-level segmentation and lesion position marking results of the user's lung CT image, the present invention analyzes the user's lung CT image to monitor and compare the lesion changes displayed by chest CT during the user's rehabilitation process in real time, and further digitally represents the changes in the user's rehabilitation process, and then further converts the digitally represented changes in the user's rehabilitation process into visual graphics, so that system end users can read them more quickly and clearly.

According to an aspect of the present invention, there is provided computer-implemented method for forecasting near-term sudden cardiovascular events, comprising: pretraining a large language model transformer architecture using a processor with an associated computer memory device to recognize text associated with sudden cardiovascular events cases reporting sudden cardiovascular events following a medical exam appointment; obtaining relevant data regarding an asymptomatic individual comprising one or more of a coronary artery calcium (CAC) scan, a coronary CT angiography (CCTA), chest CT, blood markers, and electrocardiogram; providing the relevant data of the asymptomatic individual to the trained computer-implemented artificial neural network; receiving a forecast of the chance of near-term sudden cardiovascular events in the asymptomatic individual; and recommending the next diagnostic or therapeutic step for the asymptomatic individual.

The invention provides an airway geometric parameter measurement method and system, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining airway foreground voxel data corresponding to a chest CT, and converting a voxel index coordinate into a world physical coordinate according to image space meta-information; obtaining a center line point set through three-dimensional skeletonization, establishing candidate connecting edges in a preset neighborhood, generating a topological tree based on a strategy with branch penalty terms, and completing branch segmentation and generation coding; the method comprises the steps that firstly, a tail end branch meeting a preset condition serves as a starting point for backtracking, the section area and the section equivalent diameter of each node slice are calculated, branch node direction vector score cross angle parameters are fitted, finally, a structured file is summarized and output according to algebraic labels and other dimensions, and stable measurement and standardized output of airway multi-scale geometric parameters can be achieved. And the comparability and repeatability of group scale analysis are improved.