Abstract: A method and system to generate a probabilistic prediction of the presence/absence of cancer in longitudinal and current image datasets, and/or multimodal image datasets, and the location of the cancer, is described. The method and system uses an ensemble of deep learning models. The ensemble includes a global model in the form of a 3D convolutional neural network (CNN) extracting features in the datasets indicative of the presence of cancer on a global basis. The ensemble also includes a two-stage prediction model which includes a first stage or detection model which identifies cancer detection candidates (different cropped volumes of 3D data in the a dataset containing candidates which may be cancer) and a second stage or probability model which incorporates the longitudinal datasets (or multimodal images in a multimodal dataset) and the extracted features from the global model and assigns a cancer probability p to each of the cancer detection candidates.

Abstract: Systems and methods are provided for generating a visualization of a lung fissure. Medical imaging data is processed to identify a lung mesh and fissures data. The lung mesh is augmented with the fissures data and projected onto a straight plane for rendering into a concise two-dimensional image in which completeness of the lung fissure may be detected.

Abstract: Systems and methods are provided for generating a visualization of a lung fissure. Medical imaging data is processed to identify a lung mesh and fissures data. The lung mesh is augmented with the fissures data and projected onto a straight plane for rendering into a concise two-dimensional image in which completeness of the lung fissure may be detected.

Abstract: A method for visualizing airway wall abnormalities includes acquiring\Dual Energy Computed Tomography (DECT) imaging data comprising one or more image volumes representative of a bronchial tree. An iodine map is derived using the DECT imaging data and the bronchial tree is segmented from the image volume(s). A tree model representative of the bronchial tree is generated. Then, for each branch, this tree model is used to determine an indicator of normal or abnormal thickness. Locations corresponding to bronchial walls in the bronchial tree using the tree model are identified. Next, for each branch, the locations corresponding to bronchial walls in the bronchial tree and the iodine map are used to determine an indicator of normal or abnormal inflammation. A visualization of the bronchial tree may be presented with visual indicators at each of the locations corresponding to bronchial walls indicating whether a bronchial wall is thickened and/or inflamed.

Abstract: A projector in an endoscope is used to project visible light onto tissue. The projected intensity, color, and/or wavelength vary by spatial location in the field of view to provide an overlay. Rather than relying on a rendered overlay alpha-blended on a captured image, the illumination with spatial variation physically highlights one or more regions of interest or physically overlays on the tissue.

Abstract: A method for visualizing airway wall abnormalities includes acquiring \Dual Energy Computed Tomography (DECT) imaging data comprising one or more image volumes representative of a bronchial tree. An iodine map is derived using the DECT imaging data and the bronchial tree is segmented from the image volume(s). A tree model representative of the bronchial tree is generated. Then, for each branch, this tree model is used to determine an indicator of normal or abnormal thickness. Locations corresponding to bronchial walls in the bronchial tree using the tree model are identified. Next, for each branch, the locations corresponding to bronchial walls in the bronchial tree and the iodine map are used to determine an indicator of normal or abnormal inflammation. A visualization of the bronchial tree may be presented with visual indicators at each of the locations corresponding to bronchial walls indicating whether a bronchial wall is thickened and/or inflamed.

Abstract: A method for detecting and localizing mucus plugs in digitized lung images, includes providing a digitized lung image volume comprising a plurality of intensities corresponding to a 3-dimensional grid of points, extracting a bronchial tree from said lung image, said bronchial tree comprising a plurality of branching airways terminating at terminal points, providing a model of a 2-dimensional cross section of an airway, selecting an extended point beyond a terminal point of an airway branch in a direction of said airway branch, obtaining a 2-dimensional cross section I of size m×n points from said lung image about said selected point, processing said 2D cross section I by calculating a local neighborhood function for each point in the cross section and forming a union of all local neighborhood functions, and calculating a correlation between processed 2D cross section and said airway model, wherein said correlation is indicative of the presence of a mucus plug within said airway.

Abstract: A method and system for lymph node segmentation in computed tomography (CT) images is disclosed. A location of a lymph node in a CT image slice is received. Intensity constraints are determined based on a histogram analysis of the CT image slice, and a spatial analysis of the intensity constrained CT image slice is performed using edge detection. An initial contour is estimated based on the lymph node location and the spatial analysis. The lymph node is then segmented by propagating the initial contour using an evolving elliptical model to define the lymph node boundaries.

Abstract: An exemplary method of visualization is provided. A centerline is computed for each of a plurality of ribs in a three-dimensional (3D) image. The each of the plurality of ribs is straightened based on the centerline computation. A two-dimensional (2D) image is generated based on the straightened ribs.

Abstract: A method for assigning a lymph node in a medical image with an anatomical name, the method including: identifying landmarks in a medical image; computing features relative to the landmarks given a location of a lymph node in the medical image; and assigning an anatomical name to the location of the lymph node by using a classifier that compares the computed features with classification rules.

Abstract: A method for matching tree-structures using original image data includes providing a first tree representative of an anatomical structure in a first digital medical image of a pair of digital medical images, said tree comprising a plurality of double linked, directed branches B=(S, P, C) of sites S, links to parents P, and links to children C, providing a second tree representative of an anatomical structure in a second digital medical image of said pair of images, registering said first medical image to said second medical image wherein a registration function is defined, and matching said first tree and said second tree using said registration function.

Abstract: A computer-based method for bronchoscopic navigational assistance, including: receiving first image data of a patient's lungs, the first image data acquired before a bronchoscopy is performed; receiving second image data of a portion of one of the patient's lungs that includes a bronchoscope, the second image data acquired during the bronchoscopy; and performing image registration between the first image data and the second image data to determine a global location and orientation of the bronchoscope within the patient's lung during the bronchoscopy.

Abstract: 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.

Abstract: A branch extension method and system for segmenting airways in 3D image data is disclosed. An initial airway segmentation is obtained from the 3D image data. Terminal branches of segmented airways of the initial airway segmentation are identified. The segmentation of the terminal branches is then extended. The segmentation of the terminal branches can be extended using various segmentation techniques. This method can use complex segmentation techniques to extend the terminal branches without having a large impact to the overall speed of the segmentation.

Abstract: A method and system for detecting airways in 3D lung image data is disclosed. The 3D lung image data is filtered using one or more filters based on first and second order derivatives of the CT image data. Each filter calculates a value for each voxel of the 3D lung image data, and the values from all of the filters are combined to determine a voxel score for each voxel. If the voxel score for a voxel is greater than or equal to a threshold value, the voxel is considered an airway candidate.

Abstract: A method for visualizing airways in chest images, includes: computing a distance map of a segmented bronchial tree; extracting data from the segmented bronchial tree using the distance map; and visualizing a three-dimensional (3D) image of the segmented bronchial tree color-coded according to the extracted data.

Abstract: A method for detecting polyp candidates in tagged stool or non-tagged stool images without performing stool subtraction, includes: applying a filter to locations in image data of a colon including tagged or non-tagged stool to generate a response image based on a gradient magnitude, angle, and radius in relation to another location in the image data for each of the locations, wherein the locations are indicated in the response image as tagged or non-tagged stool based on their density or brightness within the response image; and selecting locations in the response image above a threshold as polyp candidates.

Abstract: A method for grouping airway and artery pairs, includes: computing a two-dimensional (2D) cross-section of an airway; identifying regions of high-intensity in the 2D cross-section; computing a first indicator for each of the high intensity regions, wherein the first indicator is an orientation measure of the high intensity region with respect to the airway; computing a second indicator for each of the high intensity regions, wherein the second indicator is a circularity measure of the high intensity region; computing a third indicator for each of the high intensity regions, wherein the third indicator is a proximity measure of the high intensity region with respect to the airway; summing the first through third indicators for each of the high intensity regions to obtain a score for each of the high intensity regions; and determining which of the high intensity regions is an artery corresponding to the airway based on its score.

Abstract: A method for evaluating an airway in a bronchial tree, includes: segmenting a bronchial tree; modeling the segmented bronchial tree; computing a first ratio for an airway in the segmented and modeled bronchial tree, wherein the first ratio is a ratio between a diameter of the airway lumen and a diameter of an artery accompanying the airway; computing a second ratio for the airway, wherein the second ratio is a ratio between the diameter of the artery and a thickness of the airway wall; or computing a tapering index for the airway, wherein the tapering index indicates a tapering of the diameter of the airway lumen; scoring and color coding the first ratio, second ratio or tapering index; and visualizing the segmented and modeled bronchial tree color coded according to the first ratio, second ratio or tapering index.

Abstract: A method for determining a size of an airway lumen and a thickness of an airway wall includes: computing a centerline of an airway; computing a three-dimensional (3D) gradient of a volume of the airway within a first threshold; positioning a tube along the centerline; iteratively expanding the tube by increasing its radius until the radius of the tube reaches the first threshold; determining inner and outer radii of the tube by checking the 3D gradient computed along an x-axis and a y-axis of the tube at a boundary of the tube at each iteration; and fitting the tube to the airway by using the determined inner and outer radii, wherein the inner radius of the fit tube is half a diameter of the airway lumen and the outer radius of the fit tube minus the inner radius of the fit tube is a thickness of the airway wall.