162 results about "Lung region" patented technology

Apparatus, systems, methods, and kits are provided for isolating a target lung segment and treating that segment, usually by drug delivery or lavage. The systems include at least a lobar or sub-lobar isolation catheter which is introduced beyond a second lung bifurcation (i.e., beyond the first bifurcation in a lobe of the lung) and which can occlude a bronchial passage at that point. An inner catheter is usually introduced through the isolation catheter and used in cooperation with the isolation catheter for delivering and/or removing drugs or washing liquids from the isolated lung region. Optionally, the inner catheter will also have an occluding member near its distal end for further isolation of a target region within the lung.

The invention describes methods of pulmonary delivery of a TNFα inhibitor to a subject having a disorder in which TNFα is detrimental, such that the disorder is treated. Also included is a method of achieving systemic circulation of a TNFα inhibitor in a subject comprising administering the TNFα inhibitor to the central lung region or the peripheral lung region of the subject via inhalation, such that systemic circulation of the TNFα inhibitor is achieved.

Disclosed are methods and devices for regulating fluid flow to and from a region of a patient's lung, such as to achieve a desired fluid flow dynamic to a lung region during respiration and/or to induce collapse in one or more lung regions. An identified region of the lung is targeted for treatment, such as to modify the flow to the targeted lung region or to achieve volume reduction or collapse of the targeted lung region. The targeted lung region is then bronchially isolated to regulate airflow into and/or out of the targeted lung region through one or more bronchial passageways that feed air to the targeted lung region. The bronchial isolation of the targeted lung region is accomplished by implanting a flow control device into a bronchial passageway that feeds air to a targeted lung region.

An aerosol transfer device (10) for medical aerosol generators comprises a body (12), fluidically coupled to a nebulizer (14) and to a patient interface (16). An ambient air intake (20) is formed into a lower body (12C). The body is shaped and configured to optimize mixing of ambient air from the ambient air intake and the aerosol generated by the nebulizer, resulting in the formation of an aerosol plume having optimum characteristics for delivery of the aerosol to the patient's pulmonary system, such as the central or deep lung regions. The shape and dimensions of the body are further designed to minimize aerosol deposition, thus improving delivery efficiency.

The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

A desired fluid flow dynamic to a lung region can be achieved by deploying various combinations of flow control devices and bronchial isolation devices in one or more bronchial passageways that communicate with the lung region. The flow control devices are implanted in channels that are formed in the walls of bronchial passageways of the lung. The flow control devices regulate fluid flow through the channels. The bronchial isolation devices are implanted in lumens of bronchial passageways that communicate with the lung region in order to regulate fluid flow to and from the lung region through the bronchial passageways.

Disclosed is a systematic way of automatically segmenting lung regions. To increase the efficiency of a lung segmentation technique, a region-based technique, such as region growing, is performed by a computer on a middle slice of the CT volume. A contour-based technique is then used for a plurality of non-middle slices of the CT volume. This allows the implementation to be multithreaded and results in an improvement in the segmentation algorithm's efficiency.

A catheter assembly and method of using the catheter assembly is provided herein. The catheter assembly has a catheter shaft having an elongated body extending about a longitudinal axis, a proximal portion and a distal portion, an outer surface, and at least one lumen extending therethrough the elongated body at least one means for sealing at least a portion of the lumen of the bronchial vessel. At least a portion of the catheter assembly is configured for insertion into at least a portion of a bronchial vessel of a target lung region. The means for sealing is moveable between a collapsed state and an expanded state. At least a portion of the vessel can be sealed by inflating the means for sealing. The catheter assembly can then be used to infuse a dialysate solution into the target lung region.

The invention discloses an image-guided lung interventional operation system which comprises an image acquiring module, a lung region reconstruction module, a lung respiratory movement model construction module, an operative instrument and operative region image space mapping module, a dynamic focus space-positioning and guide module and an another image acquiring module. The image acquiring module is connected with the lung region reconstruction module which is connected with the lung respiratory movement model construction module, the lung respiratory movement model construction module is connected with the dynamic focus space-positioning and guide module, and the another image acquiring module is connected with the operative instrument and operative region image space-mapping module which is connected with the dynamic focus space-positioning and guide module. Real-time space positioning of dynamic focuses is achieved, early diagnosis of carcinogenesis of lung nodule can be achieved, and error of space positioning precision is within 2mm.

A method for improving a thoracic diagnostic image for the detection of nodules. Non-lung regions are removed from the diagnostic image to provide a lung image. Vessels and vessel junctions of the lung(s) in the lung image are enhanced according to a first-order partial derivative of each of a plurality of voxels of the lung image. A vessel tree representation is constructed from the enhanced vessels and vessel junctions. The vessel tree representation can be subtracted from the lung image to enhance the visibility of nodules in the lung(s).

The invention provides a deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children. According to the method, a source data set is manually labeled; onthe basis of the combination of a full convolutional network semantic segmentation algorithm and a convolutional neural network algorithm, the full convolutional network semantic segmentation algorithm is adopted to perform lung region foreground segmentation on an image so as to obtain a region of interest, the extracted region of interest is inputted to a convolutional neural network model so asto train a classifier, and therefore, the category of an unknown chest X-ray image can be predicted, and the high-dimensional features of the region of interest are extracted; and a traditional imageprocessing method is adopted to extract the low-dimensional features of the region of interest; and the high-dimensional features and the low-dimensional features are used to train a non-linear classifier; and the category of the unknown X-ray image is predicted, and the type of the pneumonia of a patient can be judged. Since a main component analysis algorithm is used to perform dimensionality reduction on the features, and therefore, the amount of calculation can be reduced; and the features which have been subjected to mixed dimensionality reduction are inputted into the nonlinear classifier, and the category of the unknown X-ray image can be predicted.

The invention relates to a CT image lung lobe segment segmentation method, device and system, a storage medium and equipment. The method comprises the steps that an output lung contour in a CT image is detected, wherein the lung contour comprises an intra-lung region and an extra-lung region; in the lung contour, a machine segmentation method is used to screen out the intra-lung region, and the intra-lung region is used as a candidate region; in the 3D level of the candidate region, blood vessel segmentation and lung fissure segmentation are carried out on lung segments and lung lobes simultaneously; according to the result of blood vessel segmentation, a blood vessel tree is constructed to acquire the three-dimensional blood vessel distribution of the lungs; and the blood vessel tree andthe result of lung fissure segmentation are combined, and lung lobe segment segmentation is carried out to acquire the final segmentation result of the candidate region. According to the CT image lunglobe segment segmentation method, device and system, the storage medium and equipment, errors are effectively reduced; the diagnosis rate and accuracy rate are improved; and the limit of individual lung shape differences is eliminated.

The invention relates to a lung nodule detection method and device, a computer device and a storage medium. The method comprises the following steps segmenting the lung CT image through a three-dimensional convolution neural network segmentation model, and segmenting a lung region image; detecting suspicious nodules from the lung region image through a three-dimensional U-Net detection model; classifying the suspicious nodules through a three-dimensional dichotomous network, and removing the fake nodules. According to the lung nodule detection method and device, the computer device and the storage medium, the lung CT image is segmented through the three-dimensional convolution neural network segmentation model; the lung region image is segmented, the segmentation speed is high, and the speed of subsequent detection of the lung nodules is accelerated; meanwhile, the invention can be applied to the segmentation of the lung region of all lung CT images.

Provided is a method and apparatus for segmenting airways and pulmonary lobes. An image processing apparatus obtains a first candidate region of an airway from a three-dimensional (3D) human body image by using a region growing method, obtains a second candidate region of the airway based on a directionality of a change in signal intensity of voxels belonging to a lung region segmented from the 3D human body image, segments an airway region by removing noise based on similarity of a directionality of a change in signal intensity of voxels belonging to a third candidate region acquired by combining together the first and second candidate regions. Furthermore, the image processing apparatus segments a lung region from a 3D human body image by using a region growing method, obtains a fissure candidate group between pulmonary lobes based on a directionality of a change in signal intensity of voxels belonging to the lung region, reconstructs an image of the lung region including the fissure candidate group into an image viewed from a front side of a human body and generates a virtual fissure based on a fissure candidate group shown in the reconstructed image, and segments the pulmonary lobes by using the virtual fissure.

The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

The region extraction unit extracts lung regions from three-dimensional images of a plurality of time phases, the alignment unit aligns pixel position in the lung region extracted from each three-dimensional image between the three-dimensional images. This calculates a displacement vector field at each time phase of the three-dimensional images. The function generation unit calculates a local ventilation volume function representing a temporal change in ventilation volume at each point in the displacement vector field, and the quantification unit calculates a difference function, which is a function of difference values between the local ventilation volume function and benchmark ventilation volume function, as a quantitative value representing a difference between the local ventilation volume function and the benchmark ventilation volume function. The display control unit displays lung VR images on which the difference function is mapped on the display in time series order.

A method of automatically detecting pulmonary nodules is provided, including the operations of acquiring a chest computed tomography (CT) image, extracting a lung region from the chest CT image, extracting a group of nodule candidates from the lung region using a gray-level thresholding technique and a three-dimensional (3-D) region growing technique, and performing 3-D feature recursive analysis on all of the nodule candidates.

An anchored intra-bronchial apparatus for placement and deployment into selected airways and a method for using the same. When deployed, the apparatus creates a restriction of air flow to one or more targeted lung regions and achieves total lung volume reduction through a collapse or partial collapse of the targeted regions. The air flow valve of the apparatus includes a through lumen that permits drug delivery concurrent with lung volume reduction procedure