34 results about "Pulmonary area" patented technology

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 a method for grading and managing detection of pulmonary nodes based on in-depth learning. The method for grading and managing detection of the pulmonary nodes based on in-depth learning is characterized by comprising the steps of S100, collecting a chest ultralow-dose-spiral CT thin slice image, sketching a lung area in the CT image, and labeling all pulmonary nodes in the lung area; S200, training a lung area segmentation network, a suspected pulmonary node detection network and a pulmonary node sifting grading network; S300, obtaining pulmonary node temporal sequences of all patients in an image set and grading information marks corresponding to the pulmonary node sequences to construct a pulmonary node management database; S400, training a lung cancer diagnosis network based on a three-dimensional convolutional neural network and a long-short-term memory network. According to the method for grading and managing detection of the pulmonary nodes based on in-depth learning, the lung area segmentation network, the suspected pulmonary node detection network, the pulmonary node sifting grading network and the lung cancer diagnosis network are trained based on in-depth learning, the pulmonary nodes are accurately detected, and through the combination of subsequent tracking and visiting, more accurate diagnosis information and clinic strategies are obtained.

A method for automatically segmenting lung regions in a chest radiographic image comprising; providing an input digital chest radiograph image; preprocessing the input digital radiographic image; extracting the chest body midline and lung centerlines from the preprocessed image. Locating one-by-one, the chest body model, the spine model and the two lung models in the image based on the extracted chest body midline and two lung centerlines; and detecting the lung contours by deforming the lung shape models to converge to the true lung boundaries as a function of the extracting and locating image processing.

A three-dimensional medical image obtainment unit that obtains a three-dimensional medical image of a chest, a bronchial structure extraction unit that extracts a bronchial structure from the three-dimensional medical image, a divided lung region obtainment unit that divides, based on the divergence of the bronchial structure, the bronchial structure into plural bronchial structures, and obtains plural divided lung regions based on the plural divided bronchial structures, a distance image generation unit that generates, based on the plural divided lung regions, a distance image based on a distance between each voxel in an entire region excluding at least one of the plural divided lung regions and each of the plural divided lung regions, and a border non-existing region extraction unit that extracts, based on the distance image generated by the distance image generation unit, a border non-existing region, which does not include any borders of the divided lung regions, are provided.

The embodiment of the invention provides a lung lobe lung segment segmentation model training method and device. The problem that an existing lung lobe lung segment segmentation model training mode islow in accuracy is solved. The lung lobe and lung segment segmentation model training method comprises the steps of obtaining sample image data including a lung region mark, a lung lobe region mark and a lung segment region mark; inputting the sample image data into an instance segmentation model to obtain a lung lobe contour segmentation result and a lung segment contour segmentation result; calculating a loss function value according to the difference between the lung lobe contour segmentation result and the lung segment contour segmentation result and the difference between the lung lobe region mark and the lung segment region mark; and adjusting network parameters of the instance segmentation model based on the loss function value.

The invention relates to the measurement technology of pulmonary volume and pulmonary perfusion volume, in particular to a nuclear medicine pulmonary perfusion imaging quantitative analysis method andanalysis equipment, and a storage medium. The analysis method comprises the following steps of: preprocessing a patient examination image, and classifying images into dissection images and perfusionimages; and according to different types of images, selecting a corresponding way to process the examination image, obtaining dissected pulmonary net volume and perfused lung net volume, and carryingout calculation to obtain a perfusion effective volume ratio. The analysis method can automatically identify a pulmonary area in the image and can accurately carry out pulmonary perfusion imaging quantitative analysis.

The invention relates to a three-dimensional imaging method based on a wearable magnetocardiography three-dimensional measuring device, and the wearable magnetocardiography three-dimensional measuring device obtains all three-dimensional magnetocardiography signals of a human body through a plurality of measuring sensors fixed by an adjustable vest in a magnetic shielding space. The method comprises the following steps: screening transmitted three-dimensional magnetocardiogram signals according to preset amplitude information, carrying out noise reduction and dimension reduction processing on the screened magnetocardiogram signals by adopting an independent component analysis and empirical mode decomposition method, and establishing a three-dimensional grid model comprising a trunk region, a heart region and a double-lung region based on a CT (Computed Tomography) image of a human body, a lead field matrix of magnetic field conduction from the heart to the position of the in-vitro sensor is obtained in a forward solving mode, and magnetic field distribution, used for real-time display, of the outer membrane surface of the heart is reversely solved based on the lead field matrix and the multi-channel signals. The wearable magnetocardiogram three-dimensional measuring device can synchronously obtain the three-dimensional magnetocardiogram signals, analyzes and displays the three-dimensional magnetocardiogram signals in real time, and is low in cost and convenient to carry.

The invention discloses an image processing method, device and equipment and a storage medium, and the method comprises the steps: obtaining the image features of a lesion lung image, and the lesion lung image is a lung image where the boundary of a lung region adheres to the inner wall of a thoracic cavity; correcting the lesion lung image based on the image features, and determining the lung area volume of the lesion lung image based on the lung area contour obtained by the corrected lesion lung image; and determining a lung lobe position of the focus lung image based on the lung area volume, and extracting lung lobes based on the lung lobe position. The lung lobe area can be rapidly and accurately divided, and rapid quantitative analysis of clinical diseases can be achieved in an assisted mode.

The invention discloses an image processing method, device and equipment and a storage medium, and the method comprises the steps: obtaining a lesion lung image, and carrying out the lung region extraction of the lesion lung image based on a preset lung region contour; obtaining a left lung image and a right lung image based on the extracted lung area; and performing region division on the left lung image based on the left lung volume of the left lung image, and performing region division on the right lung image based on the right lung volume of the right lung image. The lung lobe region division precision can be improved, the lung lobe region division time can be shortened, and an auxiliary effect can be provided for quantitative analysis of lung diseases.

The invention discloses an image region extraction method, apparatus and device, and a storage medium. The method comprises the steps of obtaining a preset lung region contour and a lung image; obtaining a lung region contour to be extracted according to the lung image; correcting a wrong extraction contour in the lung region contour to be extracted by using the preset lung region contour to obtain a corrected lung region contour; and performing lung region extraction on the lung image according to the corrected lung region contour. According to the method, the complete and accurate correctedlung region contour can be extracted.

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. Pursuant to an exemplary procedure, an identified region of the lung is targeted for treatment. 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. An exemplary flow control device is configured to block fluid flow in the inspiratory direction and the expiratory direction at normal breathing pressures and allow fluid flow in the expiratory direction at higher than normal breathing pressures.

The invention discloses an image region correction method and device, equipment and a storage medium, and the method comprises the steps: obtaining the image features of all layers of images in a lungimage, and the lung image comprises a multi-layer superposed image; performing correction processing on the lung image by using associated features between optimized features of adjacent layers of images in the lung image, and obtaining a lung region contour of the lung image based on the corrected lung image; and obtaining a set lung area contour, and correcting the lung area contour by using the set lung area contour to obtain a corrected lung area contour. According to the invention, the lung region contour detection precision can be improved, and the lung image region detection time can be shortened.

A computer obtains a chest X-ray image, detects boundary lines in the chest X-ray image using a model constructed through machine learning, sets a third lung area including at least one of a first lung area or a second lung area, extracts a vascular index indicating at least one of thickness or density of at least one pulmonary blood vessel present in an area included in the third lung area, determines whether the area included in the third lung area is in an abnormal state on a basis of the vascular index and a reference index based on indices extracted in advance from an area in chest X-ray images in a normal state corresponding to the area included in the third lung area, and outputs, if determining that the area included in the third lung area is in an abnormal state, information indicating a result of the determination.