Abstract: A method includes receiving an image and classifying the image using a machine learning engine. The machine learning engine is trained using a training image, where the training image is labeled with a label associated with a three-dimensional volume responsive to one or more image factors for the training image satisfying one or more respective criteria. The image factor(s) include an image factor based on (1) the area of intersection between the three-dimensional volume and an image plane defined by the training image, and (2) the area of a projection of a face of the three-dimensional volume onto the image plane.

Abstract: A method includes receiving an image and classifying the image using a machine learning engine. The machine learning engine is trained using a training image that is labeled with a label associated with a three-dimensional volume responsive to image metrics for the training image satisfying respective thresholds. The image metrics include a first image metric based on the training image and a projection of the three-dimensional volume, and a second image metric based on pixel intensity values associated with the training image.

Abstract: A method includes receiving an image, and classifying the image using a machine learning engine. The machine learning engine is trained using a training image. When an image metric for the training image satisfies a threshold, the training image is labeled with a label associated with a three-dimensional volume. The image metric includes an image surface area metric defined by a ratio of a first amount of surface area of the training image that is contained within the three-dimensional volume to a second amount of surface area of the training image that is contained outside of the three-dimensional volume.

Abstract: A method includes receiving a plurality of images, each of the plurality of images being a two-dimensional view of a portion of a three-dimensional object; receiving a definition of a three-dimensional volume in a same frame of reference as the plurality of images; receiving a label for the three-dimensional volume; projecting the three-dimensional volume onto each of the plurality of images; determining, for each of the plurality of images an image metric; and determining whether to assign the label to respective ones of the plurality of images based on the image metric.

Abstract: A method includes defining a plurality of data storage policies, each of the plurality of data storage policies providing rules for storing data among a plurality of data storage locations, each of the plurality of data storage locations having a data storage cost and a data retrieval cost associated therewith; determining a baseline policy distribution among the plurality of data storage policies for an entity; receiving new data items corresponding to the entity; storing the new data items in the plurality of data storage locations using the plurality of data storage policies based on the baseline policy distribution; and determining, using the artificial intelligence engine, a selected one of the plurality of data storage policies to use in storing the new data items corresponding to the entity based on the data storage cost for each of the plurality of data storage locations, and the data retrieval cost for each of the plurality of storage locations.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

Abstract: Methods and systems for assessing lung function using volumetric images obtained at inspiration and expiration. The method may include processing the first and second set of images to identify known anatomical structures of the lungs, registering the first set of images to the second set of images to match voxels of the first set of images to voxels of the second set of images as matched pairs of inspiratory and expiratory voxels, calculating a continuous probability of a lung characteristic at a location of the matched pairs of voxels, and displaying the result on a display. The method may also include classifying lung tissue at each location as normal, having air trapping without emphysema, or being emphysematous.

Abstract: Analysis of pulmonary scans representative of a patient's pulmonary structure can be used to classify a patient into one or more of a plurality of populations. The patient's scan can be mapped to a reference domain and analyzed to determine one or more fissure features associated with a plurality of regions in the reference domain. Comparison of the determined fissure features with a plurality of fissure atlases, each associated with different population, can be performed to classify the patient into one or more of the populations. Data from different fissure atlases can be compared to determine regions in the fissure atlases that distinguish one population from another. Such distinguishing regions can improve the ability to classify the patient while reducing errors based on false classifications.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

Abstract: Analysis of pulmonary scans representative of a patient's pulmonary structure can be used to classify a patient into one or more of a plurality of populations. The patient's scan can be mapped to a reference domain and analyzed to determine one or more fissure features associated with a plurality of regions in the reference domain. Comparison of the determined fissure features with a plurality of fissure atlases, each associated with different population, can be performed to classify the patient into one or more of the populations. Data from different fissure atlases can be compared to determine regions in the fissure atlases that distinguish one population from another. Such distinguishing regions can improve the ability to classify the patient while reducing errors based on false classifications.

Abstract: Methods and systems for assessing lung function using volumetric images obtained at inspiration and expiration. The method may include processing the first and second set of images to identify known anatomical structures of the lungs, registering the first set of images to the second set of images to match voxels of the first set of images to voxels of the second set of images as matched pairs of inspiratory and expiratory voxels, calculating a continuous probability of a lung characteristic at a location of the matched pairs of voxels, and displaying the result on a display. The method may also include classifying lung tissue at each location as normal, having air trapping without emphysema, or being emphysematous.

Abstract: Methods and systems for assessing lung function using volumetric images obtained at inspiration and expiration. The method may include processing the first and second set of images to identify known anatomical structures of the lungs, registering the first set of images to the second set of images to match voxels of the first set of images to voxels of the second set of images as matched pairs of inspiratory and expiratory voxels, calculating a continuous probability of a lung characteristic at a location of the matched pairs of voxels, and displaying the result on a display. The method may also include classifying lung tissue at each location as normal, having air trapping without emphysema, or being emphysematous.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.

Abstract: Analysis of pulmonary scans representative of a patient's pulmonary structure can be used to classify a patient into one or more of a plurality of populations. The patient's scan can be mapped to a reference domain and analyzed to determine one or more fissure features associated with a plurality of regions in the reference domain. Comparison of the determined fissure features with a plurality of fissure atlases, each associated with different population, can be performed to classify the patient into one or more of the populations. Data from different fissure atlases can be compared to determine regions in the fissure atlases that distinguish one population from another. Such distinguishing regions can improve the ability to classify the patient while reducing errors based on false classifications.

Abstract: 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.

Abstract: Methods and systems for assessing lung function using volumetric images obtained at inspiration and expiration. The method may include processing the first and second set of images to identify known anatomical structures of the lungs, registering the first set of images to the second set of images to match voxels of the first set of images to voxels of the second set of images as matched pairs of inspiratory and expiratory voxels, calculating a continuous probability of a lung characteristic at a location of the matched pairs of voxels, and displaying the result on a display. The method may also include classifying lung tissue at each location as normal, having air trapping without emphysema, or being emphysematous.

Abstract: 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.

Abstract: Systems and methods for displaying a predicted outcome of a lung volume reduction procedure for a patient including a user interface, a processor, and programing operable on the processor for displaying a predicted outcome of the bronchoscopic lung volume reduction procedure on the user interface, wherein displaying the predicted outcome of the lung volume reduction procedure includes receiving patient data comprising volumetric images of the patient, analyzing the volumetric images to identify one or more features correlated to treatment outcome prediction, predicting an outcome for a treatment modality or treatment device using the one or more identified features, and displaying the predicted outcome on the user interface.