21 results about "Lung segmentation" patented technology

A system and method for computing a lung fibrosis metric are described. The system has an input interface to receive initial lung imaging data for the patient, a trained neural network lung segmentation model to generate lung segmentation data from the initial lung imaging data, a fibrosis model pre-processor to apply the lung segmentation data to the lung imaging data to produce modified lung imaging data, a trained neural network lung fibrosis model to generate fibrosis segmentation data from the modified lung imaging data, a fibrosis model post-processor to process the fibrosis segmentation data in combination with the lung segmentation data to generate labelled voxel data, a fibrosis metric processor to use the labelled voxel data to compute a fibrosis volume metric for the patient, and an output interface to provide the fibrosis volume metric as the lung fibrosis metric for the patient.

The invention discloses a chronic obstructive pulmonary disease detection method and system based on multi-instance learning and multi-task learning, and the method comprises the steps: collecting and processing the multi-omics data of a subject, and manually marking the CT slice images of a part of the subject to construct a first training set; constructing a lung segmentation model based on a U-Net network, and training by using the first training set; using the trained lung segmentation model to obtain CT slice images containing lungs corresponding to all subjects, and constructing a second training set; improving the DSMIL network based on a multi-instance learning method and a multi-task learning method to construct a chronic obstructive pulmonary disease detection model, and training by using the second training set; utilizing the trained lung segmentation model to obtain a CT slice image containing the lung of the subject, and then utilizing the trained chronic obstructive pulmonary disease detection model to obtain a corresponding chronic obstructive pulmonary disease detection result. According to the method, the feature extraction capability of the model can be improved, the generalization capability of the model is enhanced, and high accuracy of chronic obstructive pulmonary disease detection is ensured.

The present invention discloses a method, device and computing equipment for pushing image reports based on RPA and AI. The method includes: using a preset lung segmentation model to segment the lung medical image to be detected sent by the RPA robot to obtain a target medical image containing only the lung area, using a preset lung nodule detection model to detect the target medical image to obtain attribute information of each suspected lung nodule, using a preset lung nodule segmentation model to segment the area where each suspected lung nodule is located to obtain three-dimensional contour information of each suspected lung nodule; generating a suspected lung nodule image report based on the attribute information and three-dimensional contour information of each suspected lung nodule and sending it to the hospital platform through the RPA robot. In this way, the lung nodules are detected by AI image analysis technology to obtain a suspected lung nodule image report, and the report is sent to the hospital platform through the RPA robot, which reduces the time doctors spend identifying lung nodules and improves efficiency.

The invention discloses an image processing method and device, and the method comprises the steps: enabling a first lung image to comprise a lung, a bronchus part having a first medical position relation with the lung, and an artery part having a second medical position relation with the lung, and enabling the first lung image to be inputted into a first bronchus segmentation model for segmentation, obtaining a bronchus segmentation result; segmenting the lung based on a plurality of lung segmentation models constructed based on the first medical position relationship and the bronchial segmentation result to obtain a lung segmentation result, the plurality of lung segmentation models including segmentation models constructed for different lung segments; and obtaining an artery segmentation result based on the lung segmentation result, the second medical position relation and an artery segmentation model. According to the technical scheme, the accuracy of artery segmentation can be improved.

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 relates generally to assessing abnormal patterns associated with a disease from x-ray images, and in particular to assessing abnormal patterns associated with COVID-19 (coronavirus disease 2019) from x-ray images using a machine learning network trained on DRRs (digital reconstructed radiographs) and ground truth derived from CT (computed tomography). Systems and methods of assessing a disease are provided. An input medical image of a first modality is received. A lung is segmented from the input medical image using a trained lung segmentation network, and an abnormal pattern associated with the disease is segmented from the input medical image using a trained abnormal pattern segmentation network. The trained lung segmentation network and the trained abnormal pattern segmentation network are trained based on 1) synthetic images of the first modality generated from training images of a second modality and 2) target segmentation masks for the synthetic images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lung and the segmented abnormal pattern.

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 invention discloses an asthma lung image processing system based on lung tissue data, which relates to the technical field of medical image processing, and comprises a data acquisition module, a lung segmentation module, a feature extraction module, a visualization module, a lesion analysis module and a data storage module, the lesion analysis module is used for judging the condition of the patient according to the lung data, the lesion analysis module comprises a lung expansion unit, an airway evaluation unit and an asthma risk evaluation unit, and the lung function and the airway obstruction condition are quantitatively analyzed by evaluating the lung expansion value and the airway stenosis value of the patient and combining the actual breathing condition; the method is advantaged in that the asthma risk values are acquired, the risk threshold values are set, the asthma risk values are divided into different grades, doctors are helped to make personalized treatment schemes, subjective errors are reduced, lung health conditions of patients are accurately evaluated, and accuracy and quality of asthma treatment are improved.

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 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 invention provides an nnunet-based lung eighteen-segment segmentation and post-processing correction method and system, which are applied to the technical field of medical image processing, and are characterized in that left and right lung segmentation is carried out on CT image data by adopting a lightweight left and right lung segmentation model, the left and right lungs are independently processed through region cutting, and finally, a hierarchical post-processing correction mechanism is introduced, so that the correction accuracy of the lung eighteen-segment segmentation and post-processing correction is improved. And the anatomical rationality and accuracy of the segmentation result are ensured. In conclusion, by optimizing the model structure and introducing the refined post-processing correction strategy, the lung eighteen-segment segmentation efficiency is improved, the resource consumption is reduced, the accuracy and reliability of the segmentation result are remarkably improved, and a better technical solution is provided for clinical medical image analysis.

The present invention discloses a lung field image segmentation method, device, and storage medium. The method comprises: determining the gradient amplitude of a preprocessed chest image and integrating the gradient amplitude; determining a chest image based on the integration result, and segmenting the chest image into a left chest image and a right chest image based on a left lung segmentation point and a right lung segmentation point; performing costal margin segmentation, lung apex segmentation, and transverse and mediastinum segmentation on the left chest image and the right chest image, respectively, to obtain left and right costal margin segmentation images, left and right lung apex segmentation images, and left and right transverse and mediastinum segmentation images; determining lung apex region boundaries, transverse mediastinum region boundaries, and costal margin region boundaries based on the left and right costal margin segmentation images, the left and right lung apex segmentation images, and the left and right transverse and mediastinum segmentation images, and connecting the lung apex region boundaries, transverse mediastinum region boundaries, and costal margin region boundaries to obtain a lung field image. The present invention segments and connects the costal margin, lung apex, and transverse mediastinum regions of the chest, thereby improving segmentation accuracy.

The invention relates to the technical field of image processing, and discloses a lung parameter measurement system and method based on a lung CT cross section and a medium. The system comprises an image acquisition module used for acquiring a lung CT image; the left and right lung segmentation module is used for processing the lung CT image by using a deep learning model or an image processing algorithm to generate left and right lung segmentation masks; the bone segmentation module is used for processing the lung CT image by using a deep learning model or an image processing algorithm to generate a bone segmentation mask; the front-back diameter positioning module is used for positioning the front-back diameter of the front-back midline according to the left-right lung segmentation mask and the bone segmentation mask, and determining the front-back diameter of the left lung and the front-back diameter of the right lung; and the transverse diameter positioning module is used for positioning the thoracic transverse diameter according to the left and right lung segmentation masks, the skeleton segmentation masks and the front and back diameters of the front and back midlines, and determining the left lung transverse diameter and the right lung transverse diameter. The reliability of lung development parameter measurement is improved, and an accurate and consistent parameter measurement method is provided for children lung development research.

The application discloses a honeycomb lung segmentation method based on class imbalance and multi-stage down-sampling feature fusion, and relates to the field of medical image processing. The method comprises the following steps: S1, acquiring multiple honeycomb lung CT images, and dividing a training set and a test set; S2, constructing a honeycomb lung segmentation network model; S3, training the honeycomb lung segmentation network model in S2 by using the training set to obtain a trained honeycomb lung segmentation network model; and S4, inputting the test set into the trained honeycomb lung segmentation network model in S3 to obtain segmented honeycomb lung CT images. The method is helpful to improve the segmentation precision of the model on the basis of increasing a small amount of model parameters.

This application relates to the field of intelligent medicine, and particularly to an automatic classification method, device and program product for pulmonary blood perfusion based on dual-energy CT. It includes obtaining the dual-energy CT scan data of the person to be tested, where the dual-energy CT scan data includes CT images and PBV images; performing lung segmentation on the CT images to obtain the segmented CT images; registering the segmented CT images with the PBV images to obtain the registered functional images; extracting functions based on the registered functional images to obtain function information; and dividing the pulmonary functional regions through the function information to obtain the division result. This application combines dual-energy CT images and deep learning technology to provide a fast, accurate and non-invasive pulmonary perfusion assessment tool for clinicians, which is particularly suitable for the diagnosis and differentiation of pulmonary vascular diseases and has good clinical value.

The present invention provides an X-ray image lung segmentation method based on a four-convolution-layer stereo pyramid network. It mainly includes: obtaining a CXR image dataset, and performing data augmentation processing on the CXR image dataset to generate a training dataset; constructing a four-convolution-layer stereo pyramid network structure, and training the four-convolution-layer stereo pyramid network structure based on the training dataset to obtain optimal network structure parameters, wherein the four-convolution-layer stereo pyramid network structure includes four encoder blocks, four decoder blocks and a fusion module that are symmetrically arranged; obtaining the CXR image to be processed and inputting it into the four-convolution-layer stereo pyramid network structure applying the optimal network structure parameters for processing, and finally outputting the segmentation result. The present invention uses a relatively low number of parameters to solve the problem of imperfect lung boundary segmentation in gray-scale CXR images due to pathological conditions or poor imaging quality, providing an important reference basis for the confirmation of lung lesions.

The invention provides an AI-based medical respiratory system diagnosis auxiliary method and system, and the method comprises the steps: obtaining a chest CT image from a PACS system, carrying out the preprocessing of lung segmentation, window level adjustment and the like, and generating a concept score vector through a concept activation encoder; the method specifically comprises the steps that a backbone network extracts a preprocessed image feature map, similarity is calculated according to the feature map and a predefined concept prototype library to generate an activation map, a high activation area of each concept prototype is determined, and average similarity is calculated to obtain a concept score; a diagnosis probability is generated by a microcosmic concept inference device, that is, output is generated by applying logic atoms (weight is set based on medical priori knowledge), and the diagnosis probability is generated through a classification layer in combination with a concept score vector; and finally, generating a structured report according to the diagnosis probability and the concept score vector. According to the method, diagnosis is decoupled into concept discovery, quantification and reasoning, interpretable decision making is achieved, the black box problem is solved, and a doctor can conveniently trace the AI judgment basis.

The invention discloses a lung ventilation-perfusion development area image fusion method, and relates to the technical field of image fusion, and the method comprises the steps: arranging lung medical image data according to a slice sequence, and obtaining a first image data set; performing data processing on the first image data set to generate a second image data set, and inputting the second image data set into a U-Net model for lung segmentation processing to obtain a lung region mask; performing fusion processing on the ventilation development image and the perfusion development image after data processing to obtain a first fusion image, and generating a plurality of lung sub-regions; respectively extracting the ventilation development intensity and the perfusion development intensity of each lung sub-region, and calculating the ventilation perfusion ratio of each lung sub-region; and performing color coding mapping on each lung sub-region according to the ventilation perfusion ratio to generate a second fusion image. According to the method, the problem of accurate color coding and visual image fusion of the ventilation perfusion ratio of the lung self-adaptive sub-region is solved.

Devices, methods, and systems are provided for analyzing lung imaging data. A server computing device receives imaging data of a lung over a network from a client computing device and analyzes the imaging data to identify lung, airways, and blood vessels, segment the lung into lobes, subtract airways, calculate volumes, calculate emphysema scores, identify fissure locations, calculate fissure completeness scores. A reconstruction of the fissures indicating locations where the fissures are incomplete and a report comprising fissure scores, volumes, and emphysema scores are created.

The present disclosure relates to a lung segmentation, lung disease evaluation method and device, electronic equipment and storage medium, and relates to the technical field of DR lung image segmentation. The segmentation method comprises: acquiring a segmentation model of a preset convolutional neural network, a DR lung region label image used for training the segmentation model, and a plurality of DR lung images to be segmented at multiple moments in a breathing process or a breath-holding state; wherein the method for determining the DR lung region label image used for training the segmentation model comprises: respectively performing rib edge boundary, lung apex boundary, and mediastinum and diaphragm edge detection on left chest images and right chest images of the plurality of DR lung region images to obtain the DR lung region label image; training the segmentation model by using the DR lung region label image; and completing left lung and / or right lung segmentation of the plurality of DR lung images to be segmented based on the trained segmentation model. Lung region segmentation and lung disease evaluation of DR lung images can be realized.

The application provides a kind of based on double knowledge graph's pneumoconiosis X-ray chest radiograph classification method, device, equipment and storage medium, it is related to medical image processing technical field.The method comprises: after lung segmentation is carried out to chest radiograph, utilize the frozen CLIP visual encoder and its embedded multilevel residual adapter output multistage enhanced visual features to strengthen subtle lesion expression;Based on medical classification standard, construct text and visual double knowledge subgraph, carry out graph convolution interaction and fusion to original text features and subgraph, obtain optimized text features;Visual and text features are mapped to unified semantic space, the similarity of hierarchical alignment is calculated, and the classification result is obtained by attention mechanism fusion.The present application introduces medical priori through knowledge graph, combined with multilevel visual enhancement, can significantly improve the recognition ability of pneumoconiosis lesion and cross-modal semantic alignment accuracy, realize high-accuracy automatic classification.