61 results about "Pulmonary parenchyma" patented technology

The invention discloses a CT image pulmonary nodule detection and identification method based on an improved YOLOv8 model, and belongs to the technical field of target detection, and the method comprises the steps: obtaining a lung CT image from an original mhd file in an LUNA16 data set, and generating a lung CT image original data set; s2, lung parenchyma segmentation is carried out on the lung CT image original data set generated in the step S1, the lung CT image original data set is optimized, and a region of interest is reserved; the YOLOv8 model is improved; and S3, extracting image features of the training set of the original data set of the lung CT image by using the improved YOLOv8 model obtained in the step S3 to obtain a trained CT image pulmonary nodule detection and recognition model, and testing on a test set. According to the CT image pulmonary nodule detection and identification method based on the improved YOLOv8 model, the generalization ability of the detection model and the CT image pulmonary nodule identification precision are effectively improved, and the size and the position of the pulmonary nodule in the CT image can be detected more accurately.

The embodiment of the invention provides an emphysema CT image segmentation method based on multi-feature dynamic fusion and boundary perception, and is applied to the field of medical lung CT image segmentation, the method obtains an original image by obtaining an emphysema CT image and preprocessing the emphysema CT image, and the preprocessing comprises extraction and standardization of a lung parenchyma region of interest; inputting the original image into the trained emphysema CT image segmentation model for segmentation to obtain an image segmentation result; wherein the emphysema CT image segmentation model comprises an encoder and a decoder, the encoder comprises a convolutional neural network branch, a Transform branch and a dynamic fusion module, the convolutional neural network branch is parallel to the Transform branch, and the decoder comprises an up-sampling layer and a boundary sensing module. The method improves the accuracy and reliability of the image segmentation result of the emphysema CT image.

The invention discloses a novel interstitial lung disease lesion detection method based on deep learning, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining an HRCT image of a patient, and carrying out the format conversion of the HRCT image to obtain a CT image; inputting the CT image into a U-Net, and carrying out pathological pulmonary parenchyma segmentation on the CT image to obtain a segmented image; determining a lesion candidate region of the segmented image; and evaluating the lesion candidate region to determine whether the lung disease lesion occurs in the lesion candidate region. According to the method, the lesion areas of different CT images are accurately positioned, and the lesion is correctly identified and false positive areas are removed by extracting the statistical features, the shape features and the fractal features of the lesion candidate areas under the condition of interference of blood vessels and tracheas, so that the detection accuracy of novel interstitial lung disease lesion detection is greatly improved.

The invention discloses a lung three-dimensional reconstruction method and system for intraoperative positioning, and the method comprises the steps: processing a lung CT image in a DICOM format through an improved lung parenchyma segmentation algorithm, precisely extracting a lung structure through a global threshold method, connected region analysis and mathematical morphology processing, and then processing a mask generated through segmentation through a real-time smooth interpolation method, and through a dynamic parameter adaptation technology, accurate matching of a segmentation result and a gray value range of modeling data is ensured. According to the method, the problems of artifacts, redundant surfaces and gray level jump of the lung parenchyma area caused by separation of segmentation and reconstruction in a traditional method are avoided, full-process automation from data reading and lung parenchyma segmentation to three-dimensional modeling and visualization is achieved, meanwhile, a user-friendly interaction interface is designed, and a convenient tool is provided for intraoperative navigation.

The invention discloses an active / inactive pulmonary tuberculosis recognition system based on an improved 3D network, and the system comprises an image feature obtaining module which is used for obtaining pulmonary tuberculosis CT chest image features; the data preprocessing module is used for carrying out data preprocessing on the obtained pulmonary tuberculosis CT chest image features; the image segmentation module is used for segmenting the preprocessed pulmonary tuberculosis CT chest image features to obtain pulmonary parenchyma image features, and an adopted segmentation model is a 3D ResUNet segmentation model; the image classification and recognition module is used for performing prediction and classification on the lung parenchyma image features after image segmentation; wherein the adopted image classification model is a 3D ResUNet50 classification model, and the 3D ResUNet50 classification model is combined with a multi-scale attention module to obtain image output features; then, image output features are processed by adopting grouping convolution and channel shuffling, the types of active and inactive pulmonary tuberculosis are predicted, and confidence scores of the model are generated; the CT image classification accuracy of the model can be improved.

According to the chronic obstructive pulmonary disease grading system and method based on the X-ray chest radiograph, the original X-ray chest radiograph is obtained, and the original X-ray chest radiograph is preprocessed; a conditional generative adversarial network is adopted to separate the preprocessed original X-ray chest radiograph, and a bone suppression image and a bone tissue feature image are obtained; inputting the bone tissue feature image into the constructed double-branch deep neural network to obtain a pulmonary parenchyma feature map and a rib feature map; obtaining a clinical variable, preprocessing the clinical variable, performing cross-modal fusion on the preprocessed clinical variable, the lung parenchyma feature map and the rib feature map, and generating a fusion feature vector; processing the fusion feature vector by combining a regression-classification output header model to obtain an FEV1% predicted value and a GOLD staging result; a conditional generative adversarial network, a double-branch deep neural network, cross-modal fusion and a joint regression-classification output head model are introduced, and low-cost, low-radiation and high-universality GOLD automatic grading can be realized.

A lung CT blood vessel image segmentation and reconstruction system based on multiple interpretable features performs further fine-grained segmentation on the basis of main airway features through a feature extraction module to obtain a plurality of connected domains, performs multi-stage feature extraction on the connected domains, and outputs multi-scale feature information; the blood vessel segmentation module generates an information matrix according to the indexes and the pulmonary parenchyma mask, generates a blood vessel network segmentation result through an adversarial network and performs post-processing; and the model reconstruction module performs three-dimensional reconstruction on the blood vessel network segmentation result and the pulmonary parenchyma mask to obtain a 3D model. According to the method, on the basis of anatomical priori knowledge of pulmonary vessels and airways, a large amount of multi-scale feature information is utilized, the information loss of the two-stage segmentation network is reduced, and the network adaptability and the learning ability of the relation between pixels under the complex condition are improved. In the aspect of system design, a modular design and management mode is adopted, efficient resource access is achieved, and meanwhile the operability and usability of the system are improved through end-to-end process design.

The invention provides an angiography CT (Computed Tomography) image-based early-stage pulmonary artery micro embolism detection method, which comprises the following steps of: extracting pulmonary parenchyma, and segmenting a small pulmonary blood vessel to obtain a pulmonary blood vessel tree; network weight is updated based on a neural evolution calculation method, and lung arteries and veins are separated; and obtaining a complete pulmonary artery tissue from a pulmonary artery and vein separation result, establishing a level set evolution model to enable the curve to be accurately evolved to the edge of a normal pulmonary artery tissue, and removing the normal pulmonary artery tissue to obtain a pulmonary embolism focus tissue so as to obtain a pulmonary embolism detection result. The method provided by the invention can accurately detect the micro embolism part at the early stage of pulmonary embolism, and has important clinical application value.

The utility model relates to the technical field of medical instruments, and discloses a pulmonary nodule puncture positioning needle. The pulmonary nodule puncture positioning needle comprises an outer tube, an inner tube and a positioning marking assembly, the outer tube is provided with a through channel and comprises a needle tube and a needle tube seat, and the needle tube seat is arranged at the near end of the needle tube; the positioning marking assembly is arranged in the outer tube in a sliding mode, a positioning marker of the positioning marking assembly is made of shape memory materials, the inner tube is arranged in a channel of the outer tube in a penetrating mode and used for pushing the positioning marker, and the positioning marker is in a J-shaped hook shape when stretching out of the needle tube. During operation, the outer tube is inserted into a target position of a focus of a patient, the inner tube is pushed, the inner tube pushes the positioning marker out of a needle point of the puncture needle tube, and after the positioning marker is pushed out of the needle tube, the J-shaped hook is self-expanded and recovered to be a preset J-shaped hook. Resistance is formed between the J-shaped hook and pulmonary parenchyma of a patient, the J-shaped hook is positioned at the pulmonary nodule of the patient, and the J-shaped hook structure is beneficial for a doctor to touch and position in an operation and accurately determine the position of a focus.

The invention belongs to the technical field of biological medicines, and particularly relates to application of small interfering RNA (Ribonucleic Acid) of a targeted Zyx gene. The invention provides siRNA (small interfering Ribonucleic Acid) of a targeted Zyx gene, and further provides application of the siRNA in prevention and treatment of fibrosis interstitial lung diseases. Experimental data prove that in a mouse bleomycin model, siRNA of a targeted Zyx gene can effectively improve the survival rate of a sick mouse, alveolar wall thickening, lung substantive thickening and inflammatory cell infiltration of a Zyx knock-down group are not obvious, and pathological damage caused by bleomycin is effectively relieved through knock-down of the Zyx gene; collagen fiber deposition of a Zyx knock-down group is reduced, and the transcription level of fibrosis-related genes is obviously reduced.

The invention relates to the technical field of medical image processing, in particular to a lightweight pulmonary nodule detection method based on a multi-dimensional collaborative attention mechanism. According to the method, a multi-dimensional collaborative algorithm named as MS-YOLO11 is provided, based on an improved YOLO11 framework, a multi-dimensional collaborative attention mechanism and a multiple attention transformation module are fused, key feature expression is dynamically enhanced through channel, height and width dimensions, and the local and global feature interaction capability is improved in combination with pixel, channel and space attention. According to the MS-YOLO11 model, 83.66% of detection precision is obtained in an unsegmented pulmonary parenchyma CT image while only 9.35 MB model volume is maintained, the small target detection capability is effectively improved, the background interference robustness is enhanced, and the MS-YOLO11 model is suitable for pulmonary nodule auxiliary diagnosis scenes.

The invention relates to the technical field of medical informatics, and provides an interstitial lung disease HRCT image analysis method and system based on artificial intelligence. The method comprises the following steps: performing pulmonary parenchyma segmentation processing on an input HRCT image to obtain a pulmonary parenchyma region; performing multi-category lesion segmentation on the pulmonary parenchyma region to obtain a segmentation mask; performing quantitative calculation on lesion distribution characteristics according to the segmentation mask to obtain lesion distribution parameters; and carrying out image mode classification according to the segmentation mask and the lesion distribution parameters to obtain a classification result. According to the method, the efficiency and precision of HRCT image analysis are improved, and the application range of HRCT image analysis is expanded.

The invention discloses a pulmonary nodule segmentation method based on an M-N-CDFTransUNet network model, and belongs to the technical field of medical image processing, and the method comprises the following steps: carrying out the format conversion of an LIDC-IDRI lung data set image; extracting a lung parenchyma part according to K-means and an image processing mode; establishing a pulmonary nodule segmentation data set, and dividing the pulmonary nodule segmentation data set into a training set and a verification set; establishing a pulmonary nodule segmentation model of an improved TransUNet algorithm; the M-N-CDFTransUNet network pulmonary nodule segmentation model is trained, and an optimization model is obtained after training; 4, the M-N-CDFTransUNet network pulmonary nodule segmentation model obtained in the step 4 is verified; and identifying the lung image by using the verified and optimized model. According to the method, feature information of different levels can be integrated, so that the comprehensive understanding and recognition capability of pulmonary nodules is enhanced, the overall feature extraction performance of the model is improved, and the generalization capability of the model is improved.

The present invention relates to the fields of artificial intelligence and digital medical technology, and provides a lung image segmentation method, apparatus, storage medium, and computer equipment. The method comprises: constructing a lung nodule segmentation model based on a fully convolutional neural network model using a densely connected hybrid dilated convolution module and a hierarchical attention mechanism; training the lung nodule segmentation model using lung parenchyma image samples as input and lung nodule segmentation images of the lung parenchyma image samples as output to obtain a trained lung nodule segmentation model; obtaining a lung image to be processed, segmenting the lung image using a lung parenchyma segmentation algorithm to obtain a lung parenchyma image; and obtaining a lung nodule segmentation result for the lung image using the trained lung nodule segmentation model based on the lung parenchyma image. The above method can effectively improve the segmentation accuracy of lung nodules.

The application discloses a kind of X-ray film lung disease screening systems of fusing multi-level features, including lung parenchyma segmentation module, lung disease preliminary screening module, multi-level feature construction module, lung disease screening module;Lung parenchyma segmentation module is used to obtain lung parenchyma part from complete chest X-ray film segmentation;Lung disease preliminary screening module is used to construct based on feature consistency variation auto-encoding generation adversarial network and train it, and obtains preliminary screening abnormal score using trained generation adversarial network;Multi-level feature construction module is used to extract features of different semantic levels, including the extraction of bottom visual features, middle layer depth features and high layer experience features;Lung disease screening module is used to construct lung disease screening model using multi-level features and the abnormal score obtained by lung disease preliminary screening module, and obtains the final lung disease classification result.The present application can effectively solve the problem that current computer-aided lung disease screening field research is difficult to be applied to practical clinical scene.

The present disclosure is a telescopic endobronchial stem cell cultivation catheter that comprises a segment catheter (1), a terminal catheter (2), and a respiratory catheter (3) ensuring that ensures the prevention of the progression of COPD and provides early treatment before it progresses to advanced stages, allows the increase of the number of reduced lung parenchyma by means of its inoculation feature in advanced COPD stages, provides the removal of metaplastic cells in the bronchial walls in the application area with the rhythmic inflation-deflation movement by means of the balloons (1.2, 2.2, 3.2) and reduces the risk of future cancer formation, and increases respiratory ventilation by expanding narrowed terminal bronchioles (11) and increasing air flow to the respiratory bronchioles (12).

This application relates to a method, apparatus, device, medium, and program product for quantifying CT features of idiopathic pulmonary fibrosis. The method includes: acquiring a medical image of the lung to be processed; segmenting the lung parenchyma in the medical image to obtain bilateral lung regions and total lung volume; segmenting the bilateral lung regions to obtain multiple imaging features related to idiopathic pulmonary fibrosis, and determining the volume percentage corresponding to each of the multiple imaging features related to idiopathic pulmonary fibrosis and the total lung volume; and determining the predicted functional impairment value corresponding to the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis based on a pre-generated linear regression model, wherein the linear regression model represents the correlation between the volume percentage of the multiple imaging features related to idiopathic pulmonary fibrosis and various lung function indicators. This method can improve processing efficiency.

The application belongs to the technical field of image recognition, and discloses a weakly supervised interstitial lung disease lesion recognition method based on multiple example learning, which comprises the following steps: step 1: acquiring CT image samples; step 2: selecting part of the CT images, and manually labeling the lung parenchyma in the images; step 3: establishing a lung parenchyma segmentation model through a saliency segmentation algorithm, inputting the manually labeled CT image samples to perform training and testing, and obtaining a trained lung parenchyma segmentation model; step 4: training a lesion recognition model using a multiple example learning algorithm; step 5: acquiring a to-be-recognized CT image sample, inputting the lung parenchyma segmentation model to perform segmentation, and then inputting the segmented data sample into the lesion recognition model to obtain a lesion position. The application can realize the visual labeling of interstitial lung disease through a small amount of labeling, and greatly improves the recognition efficiency.

The invention discloses a lung parenchyma extraction method, device and equipment based on a CT image, relates to the technical field of medical image processing, and can improve the accuracy of lung parenchyma region extraction. The scheme comprises the following steps: acquiring an axial CT image sequence; converting the axial CT image sequence into a coronal CT image sequence; three continuous frames of images are selected from the coronal CT image sequence to form three channels of RGB images, and the RGB images are obtained; determining a first lung area according to the pixel value of the RGB image; after morphological processing and hole filling are carried out on the first lung region, a region in a lung parenchyma boundary is extracted, and a second lung region is obtained; performing image segmentation processing on the second lung region to obtain a lung segmentation region; and generating a lung parenchyma region based on the lung segmentation region and the corresponding coronal CT image.

The present invention provides a pleural adhesion type lung parenchyma segmentation method and system, the method comprising: processing an original image to obtain a first lung parenchyma image; obtaining a lung parenchyma mask convex hull, and calculating the Euclidean distance between two adjacent convex hull points in the lung parenchyma mask convex hull, and locating the area corresponding to the convex hull point whose Euclidean distance is within a threshold range as the first area to be repaired, and obtaining the slope change frequency of the first area to be repaired, and locating the area whose frequency is greater than a preset frequency as the second area to be repaired; respectively obtaining m sampling points on the convex hull points at both ends of the second area to be repaired and the lung parenchyma mask contour, and fitting a k-times non-uniform rational B-spline curve to repair the mask contour of the second area to be repaired, obtaining a first lung parenchyma mask image, and obtaining a second lung parenchyma image according to the first lung parenchyma mask image. Thus, the accuracy of lung parenchyma segmentation can be greatly improved.

The invention discloses a complete-cycle lung function magnetic resonance imaging method which comprises the following steps: in a free breathing state of a subject, acquiring lung coronal magnetic resonance images at different time points at the same anatomical position by using a gradient echo sequence, and selecting a lung aorta region R1 and a region R2 containing lung parenchyma and liver tissue; after the average signal time sequences of the R1 and the R2 are respectively subjected to high-pass filtering to remove the influence of respiration and low-pass filtering to remove the influence of heart blood flow, fitting is carried out in a cosine function form to obtain the phase of the signal, and the average signal time sequences in the R1 and the R2 are respectively reordered as RS1 and RS2 in a heart cycle and a respiration cycle according to the sizes of phase values. According to the method, the lung image is subjected to segmentation and registration and then is reordered according to RS1 and RS2, respiration and heart cycle parameters of each local area of the lung image are calculated, lung ventilation, lung perfusion, ventilation peak reaching time and perfusion peak reaching time images are generated, the local function of the lung can be non-invasively evaluated, and the method has very high clinical value.

A tool to create virtual lesions inside a 3D lung tumor image. The image can be an MRI or CT image of a patient's lung to be treated. This virtual pre procedural ablation allows the operator to identify optimal ablation sites and select the optimal ablation parameters for the given anatomy. Also, the actual procedure will be performed with the lung parenchyma surrounding the tumor preventing ultrasound to penetrate beyond the tumor walls. A diagnostic mode will guide the operator to optimize ablation parameters. This virtual pre ablation, the parenchymal ultrasound barrier and the diagnostic catheter operation should enable the operator to conduct the therapeutic procedure safe, fast and effective.

The application relates to a lung nodule detection method, device and equipment based on an improved network and a storage medium. The method comprises the following steps: processing each CT original image in an acquired image sample set to extract a corresponding lung parenchyma image, so as to reduce the amount of data to be processed and facilitate observation of the effectiveness of detection, and a lung nodule detection neural network used in the method is obtained by improving a YOLOv5 network, wherein in a backbone unit, a MetaAconC activation function is used to replace original activation functions in part of a convolution structure, a CoordAtt attention mechanism structure is added before an SPPF structure, and a BiFPN structure is used in a neck unit to perform multi-size feature fusion, so that the improved YOLOv5 network is more suitable for detection of lung nodules in medical images, and the detection is more accurate and efficient.

The present disclosure provides a CT image processing method, device, equipment, storage medium and program product, relating to deep learning technology, AI medical technology, comprising: determining a lung parenchyma region in a computed tomography (CT) image, and cutting out an initial lung parenchyma image from the CT image according to the lung parenchyma region; processing the initial lung parenchyma image to obtain a lesion segmentation mask; processing the initial lung parenchyma image to obtain a predicted lesion region in the initial lung parenchyma image; determining a target lesion region and a target lesion mask corresponding to the target lesion region according to the lesion segmentation mask and the predicted lesion region, wherein the target lesion mask is located in the target lesion region. In this implementation, the lesion region is identified by segmentation and detection, and the target lesion region and the target lesion mask inside the target lesion region are obtained by fusing the two identification results, thereby reducing the probability of missed detection.

The invention discloses a pulmonary nodule joint registration segmentation system fusing anatomical consistency and semantic priori, and the system comprises an anatomical consistency constrained registration segmentation joint learning module which is used for obtaining a reference CT image, a floating CT image and a pulmonary parenchyma mask corresponding to the reference CT image and the floating CT image in a pulmonary nodule follow-up visit, inputting the reference CT image and the floating CT image into a registration network, aligning the floating CT image to the three-dimensional dense deformation field of the reference CT image; and the semantic priori guided pulmonary nodule fine segmentation module is used for transforming the pulmonary nodule mask of the floating CT image into a coordinate system of the reference CT image based on the three-dimensional dense deformation field to obtain nodule semantic priori, and inputting the reference CT image, the nodule semantic priori and the three-dimensional dense deformation field into a pulmonary nodule fine segmentation network to realize pulmonary nodule fine segmentation of the reference CT image. According to the method, deep coordination of registration and segmentation is realized, so that more stable registration and more accurate pulmonary nodule segmentation are obtained on multi-temporal CT.

The application provides a lung nodule detection method based on deep learning, which comprises the following steps: A, performing parenchymal segmentation on a three-dimensional lung CT image; B, sequentially processing the lung parenchymal image after being divided into blocks to obtain a sparse matrix; C, extracting three-view information of the sparse matrix for false positive screening; D, building a network model; and E, using the trained network model to detect lung nodules and output the detection results. The three-view false positive auxiliary module is combined, directly embedded into an end-to-end framework, and the three-view self-attention information of the sparse three-dimensional image is used to help screen out false positive regions. On the one hand, the problem of learning attention in a complex 3D scene is solved. On the other hand, the detection and false positive region screening are designed into an end-to-end training model, the detection speed and detection accuracy are improved, the overall complexity of the model is reduced, the model training convergence speed is improved when the model is trained, and the loss function is trained uniformly.

The present disclosure is a telescopic endobronchial stem cell cultivation catheter that comprises a segment catheter (1), a terminal catheter (2), and a respiratory catheter (3) ensuring that ensures the prevention of the progression of COPD and provides early treatment before it progresses to advanced stages, allows the increase of the number of reduced lung parenchyma by means of its inoculation feature in advanced COPD stages, provides the removal of metaplastic cells in the bronchial walls in the application area with the rhythmic inflation-deflation movement by means of the balloons (1.2, 2.2, 3.2) and reduces the risk of future cancer formation, and increases respiratory ventilation by expanding narrowed terminal bronchioles (11) and increasing air flow to the respiratory bronchioles (12).

A system for detecting an air leak in the lung parenchyma and / or airway may have a heater configured to vaporize at least one fluid into an aerosol. A conduit system may include a conduit portion configured to be received in a user's airway. A motive flow source is connected to the conduit system for inducing a flow of the aerosol into the conduit portion. A controller is configured for operating the heater to generate the aerosol, and for controlling the motive flow source to induce the flow of the aerosol into the conduit portion at a flow rate of a given level. A method for detecting an air leak in the lung parenchyma and / or airway is also provided.

The invention discloses a lung structure injury degree evaluation method and device, equipment, a medium and a product, and relates to the field of intelligent medical treatment, and the method comprises the following steps: counting lung parenchyma morphological parameters under different scales after mechanical ventilation by adopting a stereology method; determining the total volume of the pulmonary alveoli according to the number of the test points falling on the pulmonary parenchyma, the number of the test points falling on the lung, the number of the test points falling on the diaphragm and the number of the test points falling on the pulmonary alveoli; determining the surface area density of the pulmonary alveolus according to the length of the test line contained in the diaphragm and the intersection frequency of the test line and the pulmonary alveolus; determining the total surface area of the pulmonary alveoli according to the total volume of the pulmonary alveoli, the surface area density of the pulmonary alveoli, the number of the test points falling on the diaphragm and the number of the test points falling on the pulmonary alveoli; determining the alveolar average linear intercept according to the alveolar total surface area and the cavity volume of the alveolar and the airway; and evaluating the injury degree of the lung structure according to the alveolar average linear intercept. The application evaluates the degree of injury of the lung structure after mechanical ventilation.

The invention discloses an RT-DETR training method for pulmonary nodule detection, and relates to the technical field of medical and industrial combination. The RT-DETR training method for pulmonary nodule detection comprises the steps of firstly obtaining an original data set from an LUNA16 data set, then performing normalization and slicing on a CT image in the original data set, performing segmentation by using a K-means algorithm to generate a pulmonary parenchyma mask and VOC data to generate a required data set, and performing random distribution on the data set; a light-weight LSNet module is introduced to replace an original model Backbone, tiny textures are strengthened while a receptive field is expanded, an EDFFN is adopted to replace a two-layer feed-forward network in an original model feature interaction module, high-value information is reserved through frequency domain gating, and artifacts are suppressed; and training and testing the improved model by using a randomly distributed data set. The improved method can effectively, quickly and accurately detect the pulmonary nodules in the lung CT image.