90 results about "Chest ct" patented technology

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 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.

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 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 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.

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.

The invention provides an intelligent tuberculosis prevention and control method and system based on big data and deep learning, and relates to the technical field of data analysis, and the method comprises the steps: building a patient contact relation network, predicting the tuberculosis transmission risk through a deep neural network model, extracting an infectious index through combining a chest CT image and a sputum bacteria detection result, and obtaining a patient contact relation network; a regional propagation risk map is generated, prevention and control resource configuration is optimized through a Monte Carlo tree search algorithm, accurate prevention and control are achieved, and when an infectious index exceeds a threshold value, a resource configuration scheme is updated in time.

The present application relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The present application firstly pre-processes chest CT images and detects candidate lesion regions; then extracts topological features, deep convolution features and texture statistical features based on persistent homology theory for each candidate lesion, performs feature fusion through a multi-head self-attention mechanism to generate a unified lesion representation vector; then maps the lesion representation vector to a pre-constructed radiology knowledge graph, performs reasoning using a graph neural network, and outputs the pneumonia suspected probability and lesion classification for each lesion; finally, the evidence theory is used to fuse and quantify the uncertainty of the analysis results of multiple lesions to generate a comprehensive screening report. The present application effectively identifies complex morphological lesions through topological features, accurately identifies lesion types through knowledge graph reasoning, and provides quantitative diagnosis uncertainty through the evidence theory.

The invention relates to a pulmonary nodule auxiliary detection method based on multichannel nonlinear density adaptive mapping, and belongs to the field of image processing, and the method comprises the steps: processing original chest CT data to generate pulmonary nodule candidate ROI image blocks; simultaneously generating a first reference channel and an enhanced mapping channel for each pulmonary nodule candidate ROI image block; for an enhanced mapping channel, according to physical density distribution of the pulmonary nodules, performing functional segment division on a physical density space, and adopting a differentiated continuous mapping mechanism to enable a target density interval where the pulmonary nodules are located to occupy a larger characterization bandwidth; and inputting into the deep neural network by adopting a joint input mode of a first reference channel and an enhanced mapping channel, and outputting one or more of a pulmonary nodule candidate probability graph, a candidate score and a candidate region. According to the invention, through joint input of the first reference channel and one or more enhanced mapping channels, the discrimination capability of a downstream detection network is improved.

The application discloses a lung bullae recognition and interventional navigation planning method based on medical topology analysis, and comprises the following steps: obtaining an affine omics feature set and a clinical variable set based on chest CT images and clinical text data; performing rule-preferred feature selection and neural symbol reasoning to generate a knowledge-enhanced feature vector; inputting the three kinds of features into a classifier to obtain lung bullae recognition probability and recognition results; inputting the chest CT images into a double-flow cascaded segmentation network to obtain a lung bullae lesion mask and an airway tree mask; performing three-dimensional skeleton extraction on the airway tree mask to obtain a center line, and constructing an airway topology graph, constructing a lung bullae three-dimensional surface based on the lung bullae lesion mask, and determining a drainage entrance node; and performing reverse backtracking search based on the drainage entrance node in the airway topology graph to obtain a target drainage bronchus path. The application breaks through the end-to-end technical chain from transparent diagnostic stratification to high-fidelity three-dimensional intelligent interventional path planning of lung bullae, and significantly reduces the risk of surgical misoperation.

This invention discloses an automated multi-parameter assessment method, device, and medium for aortic imaging based on three-dimensional images. The method includes acquiring three-dimensional chest CT images and preprocessing them; extracting and optimizing an initial centerline based on the obtained three-dimensional vascular mask; dividing the initial centerline into ascending aorta, aortic arch, and descending aorta segments using the optimized initial centerline and the opening positions of branches on the aortic arch; extracting the average CT value of the adipose tissue surrounding the descending aorta based on the divided descending aorta segments; constructing orthogonal cross sections, performing dimensionality reduction and projection processing on each cross-sectional mask, and calculating the major and minor axes based on the farthest point pairs, outputting the major axis diameter sequence of each cross section; calculating the aortic calcification load, assessing aortic dilation and morphological variations, and evaluating the inflammatory status of the perivascular adipose tissue; and generating a comprehensive report of indicators. This invention fully utilizes existing imaging resources, improving the utilization efficiency and clinical value of medical imaging data.

The embodiment of the invention provides a pulmonary nodule disease body dynamic underwriting method and device based on multi-modal data fusion, and the method comprises the steps: obtaining the basic information of a user and a chest CT report image, carrying out the OCR recognition and verification of the chest CT report image, and outputting structured JSON data; performing standardization processing and semantic extension on the medical terms extracted from the structured JSON data according to the mapping rule base to generate standardized report data; inputting the CT parameters, the dynamic follow-up visit data and the external risk factors in the report data into a lung cancer risk probability model, and calculating a lung cancer risk probability value; and step-by-step matching is carried out on the lung cancer risk probability value and other nodule characteristics with the underwriting rule, and a structured underwriting conclusion is generated.

The application provides a system for predicting the benignity or malignancy of lung nodules with a diameter of less than or equal to 1 cm based on a deep learning technology, and belongs to the technical field of disease diagnosis.The system comprises the following parts: a feature extraction module, which is used for inputting a preprocessed clinical chest CT image, performing down-sampling on the input image, and performing feature learning; a prediction module, which is used for obtaining the benignity or malignancy prediction probability of lung nodules with a diameter of less than or equal to 1 cm; and an output module, which is used for outputting the prediction result.The prediction system has high sensitivity, good specificity and high accuracy, and can solve the problem that it is difficult to judge the benignity or malignancy of lung nodules with a diameter of less than or equal to 1 cm, has important significance for early diagnosis of lung cancer, and has a good application prospect.

The invention relates to the technical field of artificial intelligence and medical image processing, in particular to a multi-stage outcome prediction method based on an improved ResNet encoder, and the method comprises the following steps: collecting a chest CT image of an acute-stage lung disease; an improved ResNet encoder module used for extracting high-dimensional image features in the chest CT image is established through an input convolution layer, four residual blocks, an average pooling layer and a linear transformation layer; and outputting short-term, medium-term and long-term outcome probabilities based on the high-dimensional image features output by the improved ResNet encoder module by using a pre-established multi-stage outcome prediction model. According to the method, a plurality of classification heads are designed to be fused in a cascade mode to realize guidance of time sequence information, an improved cross-period fusion loss function based on Focal Loss is introduced, continuous constraint among prediction results in different periods is realized, and a multi-classification-head cascade structure and a feature extraction encoder structure of a loss cross-period fusion combination are combined, so that the prediction accuracy is improved. And therefore, the performance of the multi-period prediction model can be played to the optimum.

This invention relates to a multi-organ biological age and disease risk assessment system based on chest CT radiomics, belonging to the field of medical image computer-aided analysis technology. The invention aims to address the problem that a single global image age cannot characterize organ-specific aging and lacks a closed-loop clinical risk quantification mechanism. The technical solution includes: receiving chest CT radiomics features and demographic parameters of the subject through an input layer; performing spatial mapping using a feature layer; outputting a 10-dimensional biological age through a three-stage cascaded machine learning model in the age prediction layer; calculating the age acceleration rate through a bias correction layer; quantifying the disease risk multiple by coupling a Cox proportional hazards model through a risk assessment layer; and finally, generating clinical interpretation through a large language model driven by an intelligent reporting layer. This invention achieves low-cost and non-invasive organ-level aging assessment, accurately capturing allometric aging, and improving the efficiency of clinical risk stratification and the standardization of interpretation.

The application relates to an interactive bronchoscope navigation system and method, the navigation system comprising: a CT data acquisition module for acquiring chest CT data of a patient; a navigation data acquisition device for simulating the insertion operation of a camera at the head end of an insertion tube of a bronchoscope based on the chest CT data and obtaining insertion position data and posture data of the camera; a digital bronchial tree generation module for generating a digital bronchial tree based on the chest CT data; a navigation data mapping module for mapping the insertion position data and the posture data of the camera into the digital bronchial tree, performing motion calculation of a virtual camera under the constraint of a digital airway lumen of the digital bronchial tree, obtaining the pose of the virtual camera in the digital airway lumen, and realizing simulation operation. The simulation operation of the bronchoscope can be realized, the digital airway of the patient can be preoperatively preoperated, and the operation efficiency is improved, the operation risk is reduced, and the medical cost is saved.

This invention discloses a method and system for lung interstitial imaging analysis based on clinically prior-guided feature fusion. The method includes: acquiring chest CT images and corresponding clinical data at multiple time points from the user, performing data preprocessing and region segmentation; extracting features from the automatically segmented chest CT images and corresponding clinical data using specific indicators; encoding specific time points into temporal embedding vectors, projecting the total image feature vectors through a linear layer to the same dimension as the temporal embedding vectors to generate temporal encoding fusion; actively "querying" and "weighting" the most relevant CT image follow-up time points using clinical risk factors, performing temporal image feature fusion, and outputting the weighted fused features; calculating the progression probability using the fused features, and predicting the risk of progression in the next year based on the calculation results. This invention achieves dynamic temporal feature selection driven by clinical prior knowledge.

This invention belongs to the field of medical image processing technology, specifically relating to a method and apparatus for quantitative analysis of microvessels in pulmonary nodules in chest CT images. The analysis method of this invention includes the following steps: Step 1, preprocessing the chest CT image; Step 2, segmenting the chest CT image using a convolutional neural network model to obtain images of the pulmonary airways, pulmonary vessels, and lung tissue; Step 3, detecting pulmonary nodules in the chest CT image using the convolutional neural network model, extracting lesions, and extracting microvessels around the lesions and within the pulmonary nodules; Step 4, calculating the microvessel density value based on the extraction results of Step 3. This invention also provides an apparatus suitable for the above analysis method. This invention can non-invasively, rapidly, and accurately display the microvessel density value in pulmonary nodules and around lesions in three dimensions, and provide precise quantitative data. It has significant clinical and socioeconomic value.

This invention discloses a method for constructing an interpretable predictive model of pathogenic bacteria in chest CT scans, belonging to the field of artificial intelligence technology. The method includes: Step S1: acquiring a chest CT image dataset and pathogenic bacteria type labels, and constructing a pathogenic bacteria imaging knowledge graph using a medical knowledge base; Step S2: constructing an initial model, which includes: a noise-invariant encoder, a counterfactual causal intervention module, a knowledge-guided feature compensation module, an interpretability analysis module, and a classifier; Step S3: feeding the spatial attention weight map back to the noise-invariant encoder to update and iterate the anatomical pathological features. Based on the final features obtained after internal iterative optimization, the initial model is trained according to preset training rules until overall convergence. This invention effectively distinguishes between real lesions and noise artifacts in low-dose CT images through causal intervention and attention map difference-driven feature updates, improving the model's recognition accuracy and robustness.

The application relates to the technical field of image analysis, in particular to a respiratory system lung nodule tumor cell detection method and system based on image feature fusion and a storage medium, which comprises the following steps: constructing a two-dimensional pixel gray array of a chest CT image and performing edge positioning, constructing a closed image boundary through gray difference analysis and gradient direction continuity, extracting a lung nodule candidate region image in the closed region, establishing pixel adjacency relations in horizontal, vertical and diagonal directions, constituting a direction difference image set and extracting a continuous texture region with a closed structure, generating a lung nodule edge contour connection image through spatial relationship mapping and pixel connection extension, and finally performing boundary fusion and pixel structure recombination to form a target detection image. In the application, the direction gray difference construction and boundary closure analysis are combined, the connected path judgment and pixel aggregation processing are fused, the texture structure concentration and image recognition definition are effectively enhanced, and the accuracy and readability of lung nodule detection are improved.

The present invention discloses an intelligent chest CT image processing system, including 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 present invention belongs to the field of image processing, and specifically refers to an intelligent chest CT image processing system. This solution performs differentiated processing on noise characteristics in different spectrum bands, guiding the focus on retaining and enhancing the course of blood vessels; through edge fusion correction, it takes into account the adaptive enhancement of small-scale nodules and large-scale lesions; through branch A, the nodule edge and the weak contrast of artifacts are refined, and branch B strengthens the macroscopic consistency of the lung lobe; the decoding layer suppresses the learned area, the focused blood vessels, and the intersection of the lung parenchyma through interface focusing; the loss introduces boundary smoothing, optimizes the overall segmentation contour, and reduces false positive burr-like noise; thereby improving the accuracy of chest CT image processing.