22 results about "Lung field" patented technology

The invention discloses a dynamic DR lung field area diagnosis system based on deep learning, and relates to the technical field of medical imaging, and the system comprises an image collection optimization module which is used for capturing a low-dose dynamic DR sequence of a complete period of calm respiration and best effort respiration of a patient, generating a dynamic DR image, and optimizing the dynamic DR image through employing a multi-mode transfer learning strategy, outputting a DR time sequence tensor; and the intelligent diagnosis module is used for carrying out disease cause classification through a gating chart convolutional network in combination with the lung field dilation function ratio and the space structure complexity index, outputting a classification result, and generating a structured report in combination with the lung field dilation function ratio, the space structure complexity index and the continuous lung field area change curve. The differential homeomorphic transformation field is generated through the Lie group exponential mapping algorithm, and the lung lobe boundary connectivity and the bronchial tree topological structure are strictly kept in the three-dimensional deformation process.

Provided is a diagnostic support program that is possible to display a movement of an area whose shape changes for each respiratory element including all or part of expired air or inspired air.There are provided processing of acquiring a plurality of frame images from a database that stores images, processing of specifying of a cycle of a respiratory element including all or part of expired air or inspired air based on pixels in a specific area in each of the frame images, processing of detecting a lung field based on the cycle of the specified respiratory element, processing of dividing the detected lung field into a plurality of block areas and calculating a change in image in a block area in each of the frame images, processing of Fourier-transforming a change in image in each block area in each of the frame images, processing of extracting a spectrum in a fixed band including a spectrum corresponding to the cycle of the respiratory element, out of a spectrum obtained after the Fourier-transforming, processing of performing inverse Fourier transform on the spectrum extracted from the fixed band, and processing of displaying each of the images after performing the inverse Fourier transform, on a display.

The invention discloses an intelligent diagnosis and treatment integrated system for respiratory diseases, and the system comprises a data collection module which introduces a hospital information system to record clinical data, medical images and physiological signals of a to-be-detected object; the edge calculation module is used for carrying out filtering processing on the physiological signal and carrying out noise reduction and lung field segmentation processing on the medical image; the multi-modal fusion diagnosis module is used for generating disease prediction types, severity and treatment suggestions according to the clinical data, the medical images and the physiological signals, and introducing an analysis module to generate a diagnosis report; the application service module is used for providing diagnosis and treatment feedback for the doctor end, the patient end and the management end and supporting the model optimization module to dynamically optimize the multi-modal fusion diagnosis module; according to the invention, multi-modal data is deeply fused through the multi-modal fusion diagnosis module, so that the limitation of single data is overcome; therefore, the method and the device play a role in improving the diagnosis accuracy.

To enhance reliability of breath-holding failure determination.SOLUTION: A kymographic apparatus comprises: a waveform acquisition part that acquires a lung field signal waveform regarding a lung field and a periodic signal waveform synchronous with a heartbeat from a dynamic image acquired in radiation kymography based on photographing conditions including breath-holding instructions; and a determination part that determines the occurrence of a breath-holding failure during the radiation kymography, on the basis of the result of comparison between the acquired lung field signal waveform and the acquired periodic signal waveform.SELECTED DRAWING: Figure 5

The present disclosure relates to a calm expiration phase, forced vital capacity determination and diagnosis method, system and dynamic X-ray machine, and relates to the technical field of calm expiration phase, forced vital capacity determination and diagnosis. The calm expiration phase determination method comprises: performing lung field segmentation on dynamic multiple X-ray two-dimensional chest images in the breathing process to obtain corresponding multiple X-ray two-dimensional lung field mask images; and determining the calm expiration phase in the breathing process according to multiple lung field mask areas of the multiple X-ray two-dimensional lung field mask images and lung field mask area change rates corresponding to the multiple lung field mask areas. The present disclosure can realize calm expiration phase, forced vital capacity determination and diagnosis.

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 invention relates to the field of respiratory dynamics image evaluation, and provides a dynamic DR-based respiratory dynamics analysis method, which comprises the following steps of: acquiring a dynamic X-ray video sequence of a chest through dynamic DR equipment; a pre-trained deep learning model is adopted to segment the lung field area and eliminate rib interference, and a rib-free image is generated; generating initial grid points covering the lung field based on a first frame image of the rib-free image; intercepting a template area by taking a previous frame of grid point as a center, intercepting a search area at a corresponding position of the target image, and calculating grid point displacement through pixel intensity statistical correlation matching to obtain grid point displacement data; performing polynomial fitting correction on the grid point displacement data according to grid coordinates to obtain single-frame displacement; according to the frame rate of the dynamic X-ray video sequence, single-frame displacement is converted into a real-time speed field, and the displacement is accumulated to generate a displacement field relative to the initial frame; and superposing velocity field or displacement field visualization results in the dynamic X-ray video sequence.

To support diagnosis of a disease by using an analysis image obtained by analyzing a dynamic image.SOLUTION: A controller of an analysis apparatus generates an analysis image by analyzing a dynamic image of a chest, and classifies signal values in a lung field region in the analysis image into at least three or more clusters based on magnitudes of the signal values. Then, the control unit causes a display unit to display a cluster map in which a region of each cluster in the analysis image is color-coded and a feature amount for each region of the cluster.SELECTED DRAWING: Figure 3

An image processing apparatus, including: a hardware processor that obtains a side image of a lung field radiographed from a side of a subject, identifies a first side lung field region and a second side lung field region, from the side image, causes a display to display at least one of the identified first side lung field region and second side lung field region, and calculates at least one of an area of the first side lung field region and an area of the second side lung field region, based on the first side lung field region and / or the second side lung field region; and a changer that is capable of changing at least one side lung field region between the first side lung field region and the second side lung field region through operation by a user.

The application discloses a pneumoconiosis staging recognition method and system based on multi-modal artificial imaging feature fusion, and relates to the technical field of medical image recognition. The method comprises the following steps: standardizing and pre-processing an input chest X-ray image and extracting a lung field region; in the lung field region, detecting and counting micro nodules based on clinical imaging prior knowledge, and extracting micro nodule quantity features reflecting the quantity and spatial distribution characteristics of nodular lesions; calculating the first-order entropy features of the lung field region image to quantify the complexity of lung parenchyma texture, and extracting high-dimensional features representing gray heterogeneity and structure statistical characteristics by using an imaging feature analysis method. The multi-class artificial imaging features are standardized and fused in a unified feature space, and the fused features are input into a learning classification model for pneumoconiosis period recognition. The application improves the stability and interpretability of pneumoconiosis staging recognition through multi-modal artificial imaging feature fusion modeling.

The invention provides a lung image recognition method and system for clinical diagnosis of the respiratory medicine department. The method comprises the steps that a binary mask containing complete lung parenchyma is obtained from a lung image; segmenting a target lung field image from the lung image based on the binary mask, and extracting a texture saliency map from the target lung field image; performing pixel-level fusion on the texture saliency map and the target lung field image to obtain a lung feature image, and performing multi-resolution pyramid decomposition on the lung feature image to obtain a Gaussian pyramid layer and a Laplacian pyramid layer; direction gradient histogram features and local binary pattern features are extracted from the Gaussian pyramid layer and the Laplacian pyramid layer respectively, and then a multi-resolution joint feature vector is constructed; and inputting the multi-resolution joint feature vector into a pre-trained image analysis network, and positioning a focus area in the lung image. According to the technical scheme provided by the invention, the lesion area in the lung image can be identified under the coupling interference of the anatomical structure and the pathological features.

A dynamic image analysis apparatus including a hardware processor that: obtains a chest dynamic image obtained by dynamic radiographing through radiation; extracts a lung field region from the dynamic image; calculates a feature amount about a blood flow rate, based on the lung field region; and limits a value of the calculated feature amount about the blood flow rate.

The present application relates to the technical field of X-ray digital image processing, in particular to a rib suppression method for chest DR image. The method comprises the following steps: 1) obtaining a DR original image and a rib region mask; 2) performing gray suppression on the rib region mask to obtain a gray value after rib internal suppression and a rib edge region after gray suppression; 3) extracting the DR original image to obtain a rib edge image, and performing coordinate transformation to obtain a coordinate-transformed rib edge image; 4) performing gradient calculation in two directions respectively to obtain gradient images of the rib edge in the two directions; 5) performing rib edge gradient suppression on the gradient images of the rib edge to obtain gradient images after rib edge suppression; and 6) reconstructing a gray image and replacing the rib edge region after gray suppression, wherein the gray image is a soft tissue image after rib suppression. The present application retains the texture information of the lung field region and does not cause unnatural transition of the rib edge.

Provided is a diagnostic support program that is possible to display a movement of an area whose shape 5 changes for each respiratory element including all or part of expired air or inspired air. There are provided processing of acquiring a plurality of frame images from a database that stores images, processing of specifying of a cycle of a 10 respiratory element including all or part of expired air or inspired air based on pixels in a specific area in each of the frame images, processing of detecting a lung field based on the cycle of the specified respiratory element, processing of dividing the 15 detected lung field into a plurality of block areas and calculating a change in image in a block area in each of the frame images, processing of Fourier-transforming a change in image in each block area in each of the frame images, processing of extracting a spectrum in a fixed 20 band including a spectrum corresponding to the cycle of the respiratory element, out of a spectrum obtained after the Fourier-transforming, processing of performing inverse Fourier transform on the spectrum extracted from the fixed band, and processing of 25 displaying each of the images after performing the inverse Fourier transform, on a display. 20 24 26 70 70 30 N ov 2 02 4 3 0 N o v 2 0 2 4 5 2 0 2 4 2 6 7 0 7 0

The application discloses a diffuse lung disease CT classification method based on MambaVision and density-frequency domain double-prior enhancement, acquires a chest CT sequence of a patient to be classified, pre-processes each CT slice, and extracts HU statistics of a lung field region; the pre-processed CT slice and the HU statistics are jointly input into a classification network model, the model takes MambaVision as a backbone network, and fuses a HU-SE module and an MFA module, wherein the HU-SE module generates channel attention weights by using the HU statistics, adaptively recalibrates feature channels, the MFA module decomposes feature maps into low, medium and high frequency bands and fuses the feature maps by using learnable weights, respectively enhances the inter-class discrimination ability of image features from two dimensions of density prior and frequency domain features, and outputs a slice-level classification probability vector; finally, a patient-level aggregation strategy is used to fuse multi-slice prediction results, and a patient-level diffuse lung disease classification diagnosis result is output. The application effectively improves the separability between diffuse lung disease types with similar morphologies.

This invention discloses a chest CT image processing system, including a data acquisition module, an image enhancement module, a multi-resolution fusion module, and a high-fidelity export module. This invention relates to the field of intelligent CT image processing technology, specifically a chest CT image processing system. This system performs intensity cropping and normalization on CT images, and combines logarithmic domain bias field regularization iterative correction to restore tissue contrast. First, it locates the lung field, refines the boundaries using variational segmentation, and uses mask-constrained nonlocal mean denoising within the lung to preserve small lesions. It then combines a Hessian matrix with an improved Frangi metric for structural enhancement, fusing the original image, denoised image, and enhanced image. Based on structural response and gradient, it constructs a voxel-level importance mapping, performs high- and low-resolution resampling, and seamlessly synthesizes the images according to weights, preserving high resolution and detail in key areas, improving the accuracy of lung field and lesion boundaries, while reducing computational and storage overhead and effectively avoiding artifacts.

The present invention relates to assisting scapula positioning in chest X-ray imaging. Provided is a system and related method for assisting subject positioning in chest X-ray imaging, wherein the system comprises an optical detection device (110), a user interface (130), and a processor (120), connected to the optical detection device (110) and the user interface (130). Thereby, the processor (120) is configured to receive an optical image signal of a rear view of the subject(S), determine a current positioning of a scapula of the subject(S) based on the optical image signal, wherein the current positioning of the scapula is assessed as to whether and / or to which extent it would overlap or would not overlap a lung field of the subject to be imaged and determine a feedback for positioning the subject(S) and / or its scapula based on the determined current positioning of the scapula. The user interface (130) is configured to provide the feedback for positioning the subject.

This disclosure relates to a method and apparatus, electronic device, and storage medium for determining lung field area and assessing lung development, and pertains to the field of DR image processing technology. Specifically, the method for determining lung field area includes: acquiring two-dimensional DR images of the left lung and / or the right lung at multiple time points during respiration; and determining the area of ​​the left lung and / or the right lung during respiration based on the two-dimensional DR images of the left lung and / or the right lung at the same time points. Embodiments of this disclosure can achieve lung field area determination and lung development assessment.

The application relates to a chest X-ray multi-target segmentation method, an electronic device and a storage medium, the method comprising the following steps: acquiring an annotated chest X-ray image; inputting the annotated chest X-ray image into a trained single-encoder-double-decoder multi-target segmentation network to obtain different target segmentation results; wherein the training steps of the multi-target segmentation network comprise the following steps: acquiring an annotated chest X-ray image dataset; inputting the image dataset into the single-encoder-double-decoder multi-target segmentation network for training to obtain the trained single-encoder-double-decoder multi-target segmentation network. The collaborative attention jump connection module in the network performs one screening on rib bone information and important information of lung fields, hearts and other targets, thereby effectively improving the segmentation performance of the rib bones; and the attention guide multi-scale feature selection module in the network further improves the segmentation performance of clavicles, posterior rib bones and lung fields.

The invention relates to the technical field of medical image analysis, in particular to a dynamic DR lung blood flow perfusion diagnosis system based on deep learning, which comprises a dynamic DR imaging module, the dynamic DR imaging module combines a self-adaptive dose, frame rate adjustment and multi-modal motion compensation method, and the dynamic DR imaging module is used for performing dynamic DR lung blood flow perfusion diagnosis by adopting a first acquisition mode evaluation and dynamic parameter calibration strategy. According to the method, the radiation dose can be strictly controlled, meanwhile, the collected dynamic DR image can be processed through infrared respiratory gating, personalized lung field positioning and a non-local mean filtering algorithm combined with attention guidance, it is ensured that the processed dynamic DR image data is free of artifacts, full in coverage and high in fidelity, and the image quality of the dynamic DR is greatly improved. Meanwhile, the problems that the prior art mainly develops in the two directions of static dissection imaging and dynamic function imaging, but has significant shortages and is difficult to meet the clinical requirements of low radiation, high dynamic and full view are solved.

A medical image processing apparatus of an embodiment includes processing circuitry. The processing circuitry acquires a medical image of a lung field of a patient, identifies anatomical regions constituting the thorax of the patient based on the medical image, estimates a respiratory state when the medical image has been captured based on the anatomical regions, and executes processing based on the respiratory state.

The invention relates to a calm expiration stage and forced vital capacity determination and diagnosis method and system and a dynamic X-ray camera, and relates to the technical field of calm expiration stage and forced vital capacity determination and diagnosis. The calm expiration stage determination method comprises the following steps: performing lung field segmentation on a plurality of dynamic X-ray two-dimensional chest images in the breathing process to obtain a plurality of corresponding X-ray two-dimensional lung field mask images; and determining a calm expiration stage in the breathing process according to the plurality of lung field mask areas of the plurality of X-ray two-dimensional lung field mask images and the lung field mask area change rates corresponding to the plurality of lung field mask areas. According to the embodiment of the invention, determination and diagnosis of forced vital capacity in a calm expiration stage can be realized.