153 results about "Lung lobe" 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 present invention provides a lung simulator comprising a substantially-rigid, fluid-tight, translucent housing simulating a human thoracic cavity and at least one flexible air-tight bag simulating a lung having a plurality of simulated lung lobes, and a corresponding plurality of valves, each of the plurality of valves coupled to a one of the plurality of simulated lung lobes and configured to simulate varying degrees of fluid flow resistance. In a preferred embodiment, the substantially-rigid, fluid-tight, transparent housing has isolated simulated left and right thoracic cavities therein and the at least one flexible air-tight bag is located within one of the simulated thoracic cavities. The present invention further provides a method of manufacturing a lung simulator.

Methods and systems for planning a bronchoscopic lung volume reduction procedure, such as placement of a one-way valve or biosealant or energy delivery in a patient's lungs. The system may include a processor and programming operable on the processor for planning the lung volume reduction procedure. Planning the lung volume reduction procedure by the processor may include receiving patient volumetric images, analyzing the images to identify the lobes and airway tree of the lungs, displaying a three dimensional model of the lungs, generating and displaying a suggested treatment volume on the three dimensional model, receiving a selected treatment volume from a user, generating and displaying a suggested treatment location within the airway tree, receiving a selected treatment location within the airway tree from the user, receiving a selected treatment modality from the user, and displaying a treatment plan.

An automated or semi-automated system and methods are disclosed that provide a rapid and repeatable method for identifying lung, lobe, and fissure voxels in CT images, and allowing for quantitative lung assessment and fissure integrity analysis. An automated or semi-automated segmentation and editing system and methods are also disclosed for lung segmentation and identification of lung fissures.

Systems and methods for visualizing pulmonary fissures including a processor and software instructions for creating a 3 dimensional model of the fissures. Creating the 3 dimensional model includes accessing volumetric imaging data of the patient's lungs, analyzing the volumetric imaging data to segment the lungs into lobes, using the segmented lobes to identify locations at which pulmonary fissures should be present where the lobes abut each other, analyzing the volumetric images to identify locations at which pulmonary fissures actually are present as existing fissure, comparing the locations at which pulmonary fissures should be present to the locations at which pulmonary fissures are present to identify locations of missing fissure, and creating a visual display comprising a 3 dimensional model of the pulmonary fissures including existing fissure portions and missing fissure portions, with the existing fissure portion visually distinct from the missing fissure portions.

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 present invention concerns a method for determining optimised parameters for mechanical ventilation, MV, of a subject, comprising: a) obtaining data concerning a three-dimensional image of the subject's respiratory system; b) calculating a specific three-dimensional structural model of the subject's lung structure from the image data obtained in step a); c) calculating a specific three-dimensional structural model of the subject's airway structure from the image data obtained in step a); d) calculating a patient-specific three-dimensional structural model of the subject's lobar structure from the lung model obtained in step b); e) modeling by a computer, the air flow through the airway, using the models of the airway and lobar structure of the subject obtained in steps c) and d) at defined MV parameters; f) modeling by a computer, the structural behavior of the airway and the interaction with the flow, using the models of the airway and lobar structure of the subject obtained in steps b) and c) at defined MV parameters; g) determining the MV parameters which lead to a decrease in airway resistance and hence an increase in lobar mass flow for the same driving pressures according to the model of step d), thereby obtaining optimized MV parameters. It also relates to a method for assessing the efficacy of a treatment for a respiratory condition.

The invention discloses a lung lobe segmentation method and system based on a three-dimensional convolutional neural network. The method comprises the following steps: constructing a training image data set of lung lobe segmentation; constructing a lung lobe segmentation network based on a three-dimensional convolutional neural network, performing network training, preprocessing the training imagedata set, and outputting a category probability graph to which each pixel belongs after the training is completed; calculating the loss of the category probability graph to which each pixel belongs by adopting a Dice Loss loss function, and weighting the loss of a plurality of category probability graphs to obtain total loss; setting weight attenuation and learning rate attenuation, and trainingthe network until the network converges; preprocessing a to-be-detected image, inputting the preprocessed to-be-detected image into a trained lung lobe segmentation network, and outputting a prediction result; and restoring the prediction result subjected to post-processing to the original input size of the to-be-detected image to obtain a final segmentation result. The lung lobe segmentation result can be obtained through preprocessing and network reasoning, end-to-end design is achieved, and the lung lobe segmentation efficiency and precision are improved.

A fissure detection method for lung lobe segmentation in 3D image data is disclosed. In this method, 3D lung image data is filtered using one or more filters based on at least one of planar structures coupled with vessel suppression, curvature computations, and local gradient magnitude and direction comparisons. Fissures are detected in the 3D lung image data based on the filtered 3D lung image data, and lung lobes are segmented from the 3D lung image data based on the detected fissures.

An apparatus and a method for post processing 2D image slices (110a-c) defining a 3D image volume. The apparatus comprises a graphical user interface controller (160), a 2D segmenter (170) and a 3D segmenter (180). The apparatus allows a user to effect calculation and display of a 2D segmentation of a cross section of an object shown in a slice (110a) and calculation and display of a 3D segmentation of the object across the 3D image volume, the 3D segmentation based on the object's previously calculated 2D segmentation.

The embodiment of the invention provides a pneumonia lesion segmentation method and device, and solves the problems of low accuracy and low efficiency of an existing pneumonia lesion segmentation mode. The pneumonia lesion segmentation method comprises the following steps: predicting a lesion area on medical image data of a positive level based on an image semantic segmentation model; counting thelesion area of each parallel layer, and calculating the lesion volume by combining the lesion area of each parallel layer, wherein the image semantic segmentation model is established through the following training steps: inputting all or part of marked sample data into a focus segmentation model to obtain a prediction result output by the focus segmentation model; based on a focus detection frame predicted by a focus detection model and a lung region predicted by a lung lobe lung segment segmentation model, screening out a low-level false positive region from a prediction result to obtain afalse label of sample data, and adding unmarked sample data; and rechecking the pseudo label, and marking the marked sample data to update the marked sample data.

Disclosed herein is a pressure sensing left atrial occluding implantable medical device. The implantable medical device includes a cardiac plug and a micro electro-mechanical system (“MEMS”). The cardiac plug includes an expandable lobe and an expandable disc proximal the lobe. The expandable lobe is configured to expand into an anchoring arrangement within the left atrial appendage. The expandable lobe is configured to expand into an occluding arrangement with the left atrial appendage. The MEMS is coupled to the cardiac plug proximal of the disc. The MEMS is configured to sense surrounding fluid pressure.

One aspect of the present disclosure relates to a device for simulating a thyroid surgical procedure. The device can include a removable neck model configured to be accepted into a body model. The removable neck model can include a thyroid model that includes a first lobe and a second lobe connected by an isthmus, at least four parathyroid gland models arranged posterior to the thyroid model and spread between the lobes, and a nerve model. The nerve model can include a first superior laryngeal nerve model arranged medial and posterior to the first lobe, a second superior laryngeal nerve model arranged medial and posterior to the second lobe, a first recurrent laryngeal nerve model arranged medial and posterior to the first lobe, and a second recurrent laryngeal nerve model arranged medial and posterior to the second lobe.

The invention discloses a new coronavirus pneumonia patient rehabilitation time prediction method and system based on deep learning. The method comprises the steps: obtaining multi-day CT sequence images of a new coronavirus pneumonia patient, and carrying out the preprocessing of the multi-day CT sequence images; respectively inputting into a lung lobe segmentation model and a pneumonia segmentation model, and respectively extracting the lung lobe region area and the lesion region area of multiple days; calculating according to the ratio of the lesion area to the lung lobe area for multiple days to obtain a lesion area ratio value for multiple days; and fitting a Gaussian process model by using the lesion area proportion R of multiple days to predict the rehabilitation time of the novel coronavirus pneumonia patient. According to the lung lobe and pneumonia region segmentation method, the Densenet is used as the DeepLab V3 + framework and the 3D UNet framework of the backbone to segment the lung lobe and pneumonia region, the segmentation is quick and effective, the Gaussian process can accurately predict the rehabilitation time of the patient, and a reference is provided for medical resource allocation.

The embodiment of the invention provides a segmented naming method and system for lung tracheas and blood vessels, and the method comprises the steps: extracting a trachea center line of a lung, and building a trachea tree structure according to the trachea center line; lung lobe type division is carried out on the trachea of each branch in the trachea tree structure; and classifying the lung segment of each bronchial tube by adopting a first classifier corresponding to the lung lobe type of the trachea to obtain a lung segment naming result of each bronchial tube. According to the embodimentof the invention, the tracheal center line of the lung is extracted; establishing tracheal tree structure, according to the lung segment naming method, the lung segment of each bronchus is classifiedby adopting the first classifier corresponding to the lung lobe type of the trachea, and the lung segment naming result of each bronchus is obtained, so that the lung trachea can be automatically named segment by segment through the effectively established spatial topological structure of the human tubular organ, the segment naming efficiency is improved, and the error rate is reduced.

The invention provides a lung lobe segmentation method and device based on a UNet network and a computer readable storage medium, and relates to the field of lung lobe image processing. The lung lobesegmentation method comprises the following steps: acquiring lung CT image data from an image input device; carrying out normalization processing on the input lung CT image data; screening out an intra-pulmonary region and an extra-pulmonary region from the processed image data by utilizing a 2D UNet network, and taking the intra-pulmonary region as a lung region candidate region; dividing the lung region candidate region into five lung lobe mask regions by using a 3D UNet network to obtain regions of a left upper lobe, a left lower lobe, a right upper lobe, a right middle lobe and a right lower lobe; respectively carrying out morphological processing on the five lung lobe mask regions to obtain a final lung lobe segmentation result; and storing the lung lobe segmentation result in a memory or outputting and displaying the lung lobe segmentation result on a screen of a display. The lung lobe is quickly and accurately extracted through the UNet network, the lung cancer position is positioned, and guidance is provided for doctors to diagnose and treat lung cancer.

The invention discloses a lung anatomy position positioning algorithm based on a deep learning technology, which can accurately and quickly divide lung CT, and can simply, quickly and accurately realize automatic segmentation of lung lobes based on lung CT images, thereby realizing the anatomy position positioning of lung lesions. Compared with a traditional segmentation method, the method has theoutstanding advantages that (1) the process is simple, and the end-to-end segmentation mode does not need to pay attention to other processes; (2) the multi-stage and multi-output network architecture controls the network in different stages, so that the segmentation effect is better, and the segmentation precision can be ensured to the maximum extent through a semantic-based segmentation mode; and (3) the generalization ability is strong, and the data in the training process is enhanced, so that the model can learn different and diverse data, namely, the generalization ability of the segmentation model is ensured, meanwhile, the risk of over-fitting is also avoided to a certain extent, and the geometric deformation and illumination influence of CT (computed tomography) are insensitive when lung lobe division is performed on different CT.

The invention provides a three-dimensional pulmonary lobe segmentation method based on a CT image, and belongs to the field of computer aided medicine. According to the method, marker-based watershedtransformation is mainly carried out in a CT image, and the lung parenchyma is subdivided into five lung lobes. The method comprises the following steps: firstly, segmenting pulmonary parenchyma, trachea and blood vessels by adopting an automatic segmentation algorithm, and generating cost images for watershed segmentation and mark points belonging to different lung lobes by utilizing the information of pulmonary parenchyma, trachea and blood vessels; And then carrying out watershed transformation on the basis of the obtained cost image and the mark point to obtain an initial lung fissure area; And finally, combining the initial pulmonary fissure area information with information of pulmonary parenchyma, trachea and blood vessels to generate a new cost image, and performing watershed transformation again to obtain a final pulmonary lobe segmentation result.

The invention discloses a lung lobe segmentation method based on a 3D full convolutional neural network and multi-task learning, and belongs to the field of automatic lung lobe segmentation. The method comprises the following steps: preparing lung lobe related data, and calibrating the lung lobe related data; preprocessing the original lung lobe CT image data and the lung lobe related data with the accurate label, and removing redundant background information; constructing a 3D full convolutional neural network based on multi-task learning; training the constructed 3D full convolutional neural network by using the pre-processed calibrated lung lobe related data and the synthesized learning error; and performing lung lobe segmentation on the input three-dimensional lung lobe CT image by using the trained 3D full convolutional neural network, and outputting a predicted lung lobe label. The lung lobe CT image data of the original size can be received, and the accurate lung lobe segmentation result can be automatically and rapidly generated.

The invention provides a chest digital image cardiothoracic ratio measuring method and belongs to the field of measurement and image processing. The method is mainly characterized by carrying out image preprocessing before segmentation, and based on the preprocessing technology, obtaining different skeleton images through two successive selections of different Gaussian error parameters; on the basis of constructing a reasonable filtering function, constructing a structure operator suitable for rib width for top-hat operation; finally, carrying out primary threshold processing on the images obtained after top-hat operation to obtain a primary lung lobe binary image; adjusting error parameters of the filtering function to obtain a thorax outer edge skeleton image; filling gaps between outer edge skeletons and lung lobes to obtain a lung lobe binary image; and with a rectangular region wherein the binary image locates serving as a region of interest, re-calculating the gray threshold of the region of interest, and obtaining a final lung lobe binary image. The method allows a doctor to automatically calculate the cardiothoracic ratio while reading a film, and give an accurate positioning point.

The invention provides a lung lobe segmentation method and device based on a CT image, electronic equipment and a storage medium, and solves the problem of how to perform lung lobe segmentation. The lung lobe segmentation method comprises the following steps: extracting a group of lung region images from a CT image sequence; identifying a plurality of lung fissure key points in the group of lung region images by utilizing a first neural network model; and performing lung lobe segmentation on the group of lung region images according to the plurality of lung fissure key points to obtain a groupof lung lobe segmentation images.