52 results about "Lung region" patented technology

A method for quantifying lung function using a medical image, comprising the following steps: capturing or receiving a medical image that includes anatomical information for a lung region of a patient; segmenting at least one region of abnormality in the lung region of the medical image using an artificial neural network; and predicting a quantification result with respect to lung function based on the size of the at least one region of abnormality.

The invention relates to the technical field of image enhancement processing, in particular to an image enhancement method of a chest X-ray film for evaluating cardiac insufficiency. The method comprises the following steps: firstly, obtaining a shielding region of a normal X-ray film lung region, and projecting the shielding region to a target image to obtain a shielding part; further, according to similar characteristics of a skeleton line of the foreground connected domain and an edge line of a shielding part, combining an edge fuzzy characteristic and a local gray scale difference of the connected domain, obtaining a pulmonary blood vessel information amount of a pixel point and obtaining a pulmonary blood vessel region; further matching pixel points of the pulmonary vessel areas in the X-ray films which are inclined left and right according to the gray level change similarity in the vertical direction; and finally, carrying out translation superposition on the X-ray films which are inclined leftwards and rightwards on the basis of the overlapped pixel point group and the pulmonary blood vessel information amount, fitting a fused pulmonary blood vessel part to a shielding part, obtaining a pulmonary blood vessel X-ray image, matching translation superposition to repair a shielding region, overcoming oblique distortion and improving a pulmonary blood vessel display effect.

A method of quantifying pulmonary function using a medical image includes acquiring or receiving a medical image including anatomical information for a lung region of a patient; segmenting at least one abnormal finding region in the lung region of the medical image using an artificial neural network; and predicting a quantification result related to pulmonary function based on a size of the at least one abnormal finding region.

An ultrasound therapy device for generating ultrasound therapy. The ultrasound therapy device includes a wearable structure, ultrasound transducer units, a tightening mechanism, a memory, and a processor. The wearable structure is securable to a user to transmit the ultrasound to a target therapy area of a user including at least one of a kidney region, a lung region, and a lower limb of the user. The ultrasound transducer units are attachable and repositionable in the wearable structure to generate and deliver the ultrasound to the target region. The ultrasound transducer units are arranged in an array. The array of ultrasound transducer units is mechanically moved within the wearable structure and is in contact with a material to facilitate penetration of ultrasound into the user's body.

The invention discloses a respiratory support aided decision-making method and system, and the method comprises the steps: obtaining lung image data of a patient, and carrying out the preprocessing of the lung image data, so as to extract a lung region of interest; inputting the lung region of interest into a prediction model to obtain image quantitative analysis data including lesion information; acquiring clinical index data of the patient, and fusing the clinical index data with the image quantitative analysis data to generate respiratory support comprehensive evaluation parameters; and outputting respiratory support decision auxiliary information based on the respiratory support comprehensive evaluation parameters. The respiratory support aided decision-making method and system provided by the invention aim at realizing automatic and objective analysis of the lung image, intelligently fusing the analysis result with the clinical indexes, and providing quantitative decision-making support for use of a respirator.

A method for automatically detecting regions of interest (ROIs) in lung CT images based on a multi-task convolutional model. CT scans are parsed to obtain a tensor-like image. This image is then coarsely segmented, resampled, and pixel-normalized to produce a three-dimensional CT image voxel matrix containing only the lung region. The three-dimensional image is then sliced ​​and several resulting two-dimensional cross-sectional images are fed into a two-dimensional convolutional model to obtain rough ROI outlines. Three-dimensional image blocks are then captured centered around the rough ROI outlines. A three-dimensional classifier is used to determine the probability of each ROI being a true ROI. After screening, the resulting three-dimensional image blocks containing true ROIs are fed into a three-dimensional convolutional model trained using multiple tasks to obtain precise ROI outlines. These outlines are then rendered in different colors based on the probability of the ROI being a true ROI. This method can rapidly process large batches of CT images and improve ROI detection efficiency.

The invention discloses a multi-airway leaflet lung mechanical ventilation system, and relates to the technical field of medical equipment, the multi-airway leaflet lung mechanical ventilation system comprises a controller, an air path module and a catheter module, the air path module is electrically connected with the controller, and a pipeline is connected with the catheter module; the conduit module is connected with the controller; the conduit module comprises a ventilation pipeline and a plurality of branch ventilation conduits, and the branch ventilation conduits are in pipeline connection with the gas path module; and a motion control module is arranged on the branch ventilation guide pipe and is connected with the controller. Independent ventilation of a plurality of lung areas is achieved under one device, refined ventilation treatment is conducted on different lung areas, meanwhile, real-time monitoring of ventilation conditions of different lung areas can be achieved, and the treatment effect of mechanical ventilation is improved.

A computer-implemented method for providing output data comprising an indication regarding the affliction of a patient with an infectious respiratory disease, the method comprises receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patience with the infectious respiratory disease; and proving the output data.

A method includes receiving one or more radiological images of an anatomy of a patient. The method also includes identifying a boundary of different tissue types in the anatomy of the patient based at least partially upon the one or more radiological images. The method also includes identifying one or more regions within the boundary. The one or more regions include a lung region. The method also includes identifying healthy tissue and COVID-19 tissue in the lung region. The method also includes quantifying an extent of the COVID-19 tissue in the lung region by comparing an amount of the COVID-19 tissue in the lung region to an amount of the healthy tissue in the lung region. The method also includes classifying the extent of the COVID-19 tissue in the lung region into one or more of a plurality of COVID-19 classes.

Certain embodiments provide a method for controlling airflow in a lung of a respiratory system of a patient. The method generally includes placing a device in an airway passage, wherein the device comprises: an outer wall and an inner wall defining a lumen from a proximal end to a distal end of the device and changing a volume of the lumen over time.

The application discloses a lung ventilation-perfusion imaging area three-dimensional blood flow distribution abnormality analysis method, relates to the technical field of lung function quantitative evaluation, and comprises the following steps: acquiring lung CT image data of a patient to be analyzed, performing structure segmentation on a plurality of sections of the lung CT image data to obtain ventilation phase gray scale images and blood flow perfusion phase gray scale images; on the basis of the lung CT image data, accurately extracting a non-black lung region by threshold segmentation, respectively calculating the ratio of ventilation CT values and blood flow CT values in the region, and generating lung region segmentation data; based on the three-dimensional V / Q ratio atlas, identifying abnormal sub-regions in which local ventilation blood flow ratios deviate from physiological ranges; and obtaining a blood flow distribution abnormality analysis report by performing structured analysis on the abnormal sub-regions. The application divides irregular sub-regions by performing a K-means clustering algorithm on gray scale values in each section, realizes adaptive segmentation of lung function heterogeneity structures, and improves the rationality of local ratio calculation.

The application discloses a lung ventilation-perfusion development region image fusion method, relates to the technical field of image fusion, and comprises the following steps: arranging lung medical image data according to slice sequences to obtain a first image data set; performing data processing on the first image data set to generate a second image data set, inputting the second image data set into a U-Net model to perform lung segmentation processing, and obtaining a lung region mask; performing fusion processing on ventilation development images and perfusion development images after data processing to obtain a first fusion image, and generating a plurality of lung sub-regions; extracting ventilation development intensity and perfusion development intensity of each lung sub-region respectively, calculating ventilation-perfusion ratio values of the lung sub-regions, performing color coding mapping on the lung sub-regions according to the ventilation-perfusion ratio values, and generating a second fusion image. The application solves the image fusion problem of accurate color coding and visualization of ventilation-perfusion ratio values of lung adaptive sub-regions.

The present application relates to the field of electrical impedance imaging, and discloses a regional lung perfusion delay time analysis method, an electronic device and a storage medium. A dynamic image of conductivity change caused by blood perfusion in a human thoracic cavity is obtained. A blood perfusion image of a heart region and a blood perfusion image of a lung region are separated. A reference curve of conductivity change of the heart region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the heart region. A conductivity change curve of each pixel point in the blood perfusion image of the lung region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the lung region. The conductivity change curve is divided by a sliding time window, and a regional lung perfusion delay time is calculated in each time window. The time resolution advantage of electrical impedance imaging technology is fully utilized, and the time delay of the time when the local blood volume of the lung reaches the maximum relative to the end of the ventricular diastole is reflected by calculating the regional lung perfusion delay time.

The application discloses a ventilator adjustment method, device, system and medium based on electrical impedance imaging. The method comprises the following steps: collecting chest electrical impedance data in real time by using an EIT device; obtaining impedance values of a target lung region image based on the chest electrical impedance data; obtaining ventilator parameters by using the ventilator; inputting the ventilator parameters and the impedance values of the target lung region image into a trained mathematical model to obtain a ventilation parameter prediction value output by the trained mathematical model; and adjusting parameters of the ventilator based on the ventilation parameter prediction value. By using the method, the accuracy of ventilator parameter adjustment can be improved.

The invention discloses a construction method and application of a pulmonary tuberculosis detection model, and belongs to the technical field of image processing, and the method specifically comprises the steps: obtaining a medical image with a label category from a database, carrying out the preprocessing, positioning a lung region, inputting the medical image into a convolutional neural network, obtaining a rough segmentation result of the lung image, and obtaining a final segmentation result of the pulmonary image; in combination with the uncertain segmentation edge image, identifying the segmentation edge of the lung, obtaining a lung region image, and carrying out noise reduction on the lung region image; performing feature extraction on the lung region image after noise reduction; obtaining a normal data value and an abnormal data value according to the key features; constructing a training sample set according to the normal data values and the abnormal data values; and performing model training through the training sample set to obtain a prediction model for predicting pulmonary tuberculosis. According to the invention, rapid determination of pulmonary tuberculosis is realized.

According to the three-dimensional lung ventilation imaging method and device, the lung ventilation condition of each lung area in the three-dimensional space can be presented, the operation efficiency is improved, the depth estimation of the three-dimensional lung is more accurate, the three-dimensional lung is not limited to orthogonality body position projection data of the normal position and the lateral position, and the three-dimensional lung ventilation imaging method and device are particularly suitable for bedridden patients. The method comprises the following steps: (1) inputting X-ray data of a normal position and an oblique position of a chest; (2) integrating the X-ray data of the normal position and the oblique position of the chest, matching the data of the normal position and the oblique position of the same breathing time phase, and mapping the two-dimensional data of the normal position and the oblique position into a three-dimensional space; (3) estimating a free deformation FFD parameter and a double-lung translation parameter by using the input data; (4) obtaining an optimal three-dimensional lung surface model estimation and a lung template number by using the parameters in the step (3); (5) voting according to the three-dimensional lung surface model estimation of each frame and the optimal lung template to obtain a global optimal lung template number; and (6) estimating three-dimensional lung ventilation parameters according to the sequential three-dimensional lung surface model.

The present application relates to a lung focus navigation path planning and target branch decision system, which adopts the following mode: pre-processing the input CT image, extracting the lung region CT image, segmenting the airway tree, arteriovenous and focus from the extracted lung region CT image; extracting the airway tree center line image, obtaining the key points, constructing the cube with the focus center as the center of gravity, screening the optimal operation point from the key points in the cube, and outputting the target path from the trachea entrance to the focus according to the optimal operation point; the present application comprehensively considers whether the bronchoscope can pass through the planned path and the blood vessel distribution in the operation point to focus puncture path, screens the point which meets the bronchoscope short walking distance and the operation point to focus puncture path without blood vessel distribution at the same time, and the present application improves the accuracy of path planning and is more in line with the actual clinical demand.

The invention provides a lung lesion automatic detection and diagnosis method based on a CT image, and relates to the field of medical image auxiliary diagnosis, and the method comprises the steps: S1, carrying out the multi-modal feature fusion and segmentation of an input CT image lung region; s2, detecting the pulmonary nodules based on dynamic self-adaptive expansion convolution; s3, quantifying the size of the pulmonary nodule in combination with the multi-modal features and the improved Snakes model; s4, performing dynamic tracking by adopting a space-time dynamic tracking module; s5, respectively extracting multi-dimensional features of the nodules by a feature extraction module, and providing input for a diagnosis decision; and S6, outputting benign / malignant diagnosis results by adopting the clinical diagnosis model. The method can accurately identify the position of the pulmonary nodule and judge the size of the nodule, thereby completing auxiliary diagnosis of the pulmonary nodule, and avoiding the problems of low efficiency, large human subjective factors, low accuracy and the like caused by excessive dependence on manpower.

Certain embodiments provide a method for controlling airflow in a lung of a respiratory system of a patient. The method generally includes placing a device in an airway passage, wherein the device comprises: an outer wall and an inner wall defining a lumen from a proximal end to a distal end of the device and changing a volume of the lumen over time.

This invention provides a method, electronic device, and storage medium for extracting the region of interest (ROI) of the aorta. The method includes segmenting a medical image to be extracted; obtaining the lower boundary location information of the lung region and the coordinate information of the minimum horizontal bounding rectangle based on a lung mask image; obtaining the physical distance information from the starting and ending image layers of the medical image to the lower boundary of the lung region based on the lower boundary location information of the lung region; obtaining the upper and lower boundary location information of the ROI of the aorta based on the physical distance information from the starting and ending image layers of the medical image to the lower boundary of the lung region; and extracting the ROI of the aorta based on the coordinate information of the minimum horizontal bounding rectangle and the upper and lower boundary location information of the ROI of the aorta. This invention can automatically and accurately extract the ROI of the aorta, which is not only low-cost but also highly efficient.

The present application relates to the technical field of biology, and discloses a method for rapidly evaluating pneumonia, especially COVID-19, based on artificial intelligence, which comprises the following steps: 1) obtaining lung region ultrasound images and circulatory volume ultrasound images of a subject, wherein the circulatory volume ultrasound images comprise left ventricular outflow tract ultrasound images, left ventricular long-axis section ultrasound images and inferior vena cava long-axis section ultrasound images; 2) inputting the lung region ultrasound images and the circulatory volume ultrasound images of the subject into a trained deep convolutional neural network model, wherein the trained deep convolutional neural network model comprises a trained lung ultrasound image scoring model and a trained circulatory volume ultrasound image scoring model, so as to obtain the evaluation result of pneumonia of the subject. The present application also discloses a trained deep convolutional neural network model suitable for pneumonia, especially COVID-19, a construction method of the trained deep convolutional neural network model, and a remote operation ultrasound robot comprising the trained deep convolutional neural network model.

The present application relates to the technical field of CT image analysis and processing, and discloses a method and device for predicting the severity of pneumonia, electronic equipment and a medium. The method comprises: collecting a three-dimensional lung CT image of a patient, pre-processing the three-dimensional lung CT image, and obtaining a corresponding two-dimensional CT lung region image; based on the two-dimensional CT lung region image, performing hierarchical prediction through a prediction model, and outputting a first prediction result of each CT slice in the two-dimensional CT lung region image; based on the first prediction result of each CT slice, performing severity prediction through a preset logistic regression model, and outputting a corresponding severity weight; predicting the three-dimensional lung CT image, obtaining a corresponding pneumonia evaluation value, correcting the pneumonia evaluation value and the severity weight, and determining a final prediction result; thereby improving the accuracy of the severity prediction of pneumonia, and improving the performance and medical interpretability of the machine learning model.

The present application discloses a method, device, and apparatus for extracting lung parenchyma based on CT images, which relates to the field of medical image processing technology and can improve the accuracy of lung parenchyma region extraction. The scheme includes: acquiring an axial CT image sequence; converting the axial CT image sequence into a coronal CT image sequence; selecting three consecutive frames of images from the coronal CT image sequence to form three channels of an RGB image, thereby obtaining an RGB image; determining a first lung region based on the pixel values ​​of the RGB image; performing morphological processing and cavity filling on the first lung region, extracting the region within the lung parenchyma boundary, thereby obtaining a second lung region; performing image segmentation processing on the second lung region, thereby obtaining a lung segmentation region; and generating a lung parenchyma region based on the lung segmentation region and the corresponding coronal CT image.

The present disclosure relates to a method, device and equipment for determining the phase distribution of a cardiopulmonary region signal, the method comprising: collecting a target electrical impedance tomography signal synchronized with the mechanical movement of the heart of a subject from the electrical impedance tomography signals of the chest of the subject at multiple detection times; determining an electrical impedance static image of the chest of the subject according to the target electrical impedance tomography signal; determining a heart region of interest and a lung region of interest of the subject in the electrical impedance static image respectively; determining a peak pixel point image according to the electrical impedance time variation curve corresponding to the target pixel point in the lung region of interest; calculating the sum of the pixel points in the heart region of interest and the lung region of interest, multiplying the sum of the pixel points with each peak pixel point in the peak pixel point image one by one to obtain a phase distribution map of the cardiopulmonary region signal of the subject. The voltage signal synchronized with the mechanical movement of the heart can be corresponded with the spatial position, thereby accurately reflecting the phase distribution of the blood flow signal of the cardiopulmonary region of the subject.

The invention provides a lung CT intelligent optimization and focus accurate display method based on free breathing. Relates to the technical field of medical image processing. The method comprises the following steps: acquiring a CT image of a target patient, and performing standardized preprocessing on a low-dose CT image and a standard-dose chest CT image to obtain a first preprocessed image and a second preprocessed image; performing full-automatic lung segmentation on the first preprocessed image by adopting a U-Net convolutional neural network model, and outputting a left lung region and a right lung region; and taking the second pre-processed image as a gold standard, training a deep convolutional neural network CycleGAN to obtain an image enhancement model, and performing image quality enhancement on the left and right lung regions in the first pre-processed image by using the image enhancement model to obtain an enhanced image. The chest image quality can be improved while the radiation dose of the patient is reduced.

The application discloses a severe patient acute lung injury evaluation system and method based on machine learning, relates to the field of acute lung injury evaluation, and comprises the following steps: acquiring chest multi-modal image data of a severe patient and performing standardized processing to generate a standard data set; dividing a lung region into structural blocks and extracting structural indexes, compensating for missing data through cross-modal space mapping or candidate block selection; identifying a modal conflict region and a structural mutation region to construct a scoring key region; after applying disturbance to screen an effective region, selecting a mean value or a weighted fusion strategy according to a consistency index; and combining dynamic weight to calculate a lung injury score and performing iterative optimization; the application can improve the identification accuracy of a key region of acute lung injury, and realizes accurate quantitative evaluation of the degree of lung injury.

This application provides a method and apparatus for region of interest (ROI) assessment, relating to the field of medical imaging technology. The method includes: acquiring chest images of a user at different times; performing the following for each chest image: extracting a lung region from the chest image, identifying anatomical landmarks in the lung region, determining the relative positions of different layers in the chest image based on the anatomical landmarks; determining the mapping relationship between different layers in different chest images based on the relative positions of the layers in each chest image; identifying a region of interest (ROI) within the lung region of each chest image; and generating a ROI assessment result based on the layer in which the ROI is located in the chest image and the mapping relationship between different layers in different chest images. This application can improve the accuracy of the assessment results.

The invention relates to the field of intelligent medical treatment, in particular to a construction method and device for EIT impedance changes of all lung units and a program product. Comprising the following steps: acquiring EIT pulmonary ventilation image data of a testee, including N complete respiratory cycles and distances from the unit center of each unit in a lung area to left and right bronchial mouths; calculating a maximum time constant and a minimum time constant of the lung area based on the pulmonary ventilation image data; obtaining the latest change starting point and the earliest change starting point of the lung region impedance based on the maximum time constant and the minimum time constant of the lung region; based on the distance from the unit center of each unit in the lung region to the left and right bronchus, the maximum time constant and the minimum time constant of the lung region, and the latest change starting point and the earliest change starting point of the impedance of the lung region, performing function relation fitting calculation to obtain the time constant and the impedance change starting point of each unit in the lung region; and obtaining the impedance change of each unit in the lung region based on the time constant of the unit given by the lung region and the impedance change starting point. The application has good clinical value.

The present invention provides a machine learning-based image segmentation and annotation method and system, which relates to the field of computer vision technology. The method includes: dynamically selecting three detection points in a key area of ​​the segmentation boundary based on the boundary morphology of the initial segmentation result to construct a dynamic triangular mesh unit; calculating geometric eigenvalues ​​based on the vertex coordinates of the dynamic triangular mesh unit, wherein the geometric eigenvalues ​​include the proportional relationship between the area and the side length of the triangular mesh unit; comparing the geometric eigenvalues ​​with a preset threshold range, and generating boundary coordinate correction parameters if the threshold range is exceeded; adjusting the boundary coordinates of the lung region segmentation result based on the correction parameters to generate a corrected lung region segmentation result. The present invention reduces the workload of doctors through end-to-end operations from image acquisition to lesion annotation. Based on the segmentation results, the lesion annotation unit can accurately locate the lesion position.

The present application belongs to the field of intelligent medical treatment, and particularly relates to a critical patient Pendelluft automatic detection method based on electrical impedance tomography and artificial intelligence. The method comprises: acquiring EIT data of a lung region of a patient within a monitoring period; segmenting the lung region into multiple regions, and extracting EIT data of the multiple regions to obtain EIT signals of the multiple regions; obtaining a tidal resistance characteristic based on the EIT signals, the tidal resistance characteristic comprising: a regional tidal resistance amplitude difference and an adjacent regional tidal resistance amplitude difference; calculating an EIT ventilation characteristic based on the tidal resistance characteristic, the EIT ventilation characteristic being a swing breath; and judging whether the patient has Pendelluft based on the EIT ventilation characteristic. Based on real-time acquired EIT time series data, through processing of the time series data, an index reflecting whether Pendelluft exists and its severity is extracted, and based on real-time monitoring of Pendelluft, the clinical management of a clinician on a mechanically ventilated patient is assisted.