Abstract: According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated.

Abstract: In order to provide radiation dose estimation during a treatment, one or more characteristics of the patient are used. A camera captures the patient so that the characteristics (e.g., organ position) may be derived. Radiation exposure and/or absorption are determined from the characteristics. A Monte Carlo, machine-learnt, or other model estimates the dosage for different locations in the patient. During the treatment, the dosage may be presented as a warning when exceeding a threshold or other visualization.

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: In order to provide radiation dose estimation during a treatment, one or more characteristics of the patient are used. A camera captures the patient so that the characteristics (e.g., organ position) may be derived. Radiation exposure and/or absorption are determined from the characteristics. A Monte Carlo, machine-learnt, or other model estimates the dosage for different locations in the patient. During the treatment, the dosage may be presented as a warning when exceeding a threshold or other visualization.

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 and system for automatic MR brain scan planning is disclosed. The method utilizes a set of 2D orthogonal localizer images to determine scanning planes for 3D diagnostic MR scans. A location of the mid-sagittal plane (MSP) is detected in each of a transversal localizer image and a coronal localizer image. A sagittal scanning plane is determined based on the location of the MSP in the transversal and coronal localizer images. A diagnostic sagittal MR scan is then acquired based on the sagittal scanning plane. The corpus callosum CC is segmented in a sagittal MR image slice resulting from the diagnostic sagittal MR scan. A transversal scanning plane can be determined based on a location of the CC in the sagittal MR image slice and the location of the MSP in the coronal localizer image, and a coronal scanning plane can be determined based on the location of the CC in the sagittal MR image slice and the location of the MSP in the transversal localizer image.

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: 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 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 detecting solid components in ground glass nodules (GGNs) in medical images, includes: performing an intensity-based segmentation on a segmented GGN to identify a high intensity region; and performing a shape analysis to determine whether the high intensity region is a solid component or a vessel, wherein the shape analysis comprises: computing a compactness of the high intensity region; and determining whether the high intensity region is a solid component or a vessel by using an area, a maximum distance on a distance transform map and the compactness of the region; or determining whether the high intensity region is a solid component or a vessel by scaling and normalizing the region and computing a compactness for the scaled and normalized region.

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 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 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 providing automatic detection of curvature of a spine and computation of specific angles in images of the spine includes automatically displaying the curvature of the spine as a line in an image of the spine, and computing at least one of a first angle or a second angle based on the line of the curvature of the spine.

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.

Abstract: A computer-implemented method for determining a joint space width includes providing image data for a skeleton, thresholding the image data, and performing a connected component analysis on thresholded image data. The method further includes extracting contours of the thresholded image data according to the connected component analysis, performing a skeletonization of the thresholded image data using a first fast marching analysis of the thresholded image data, locating at least one finger joint of skeletonized image data, extracting bone boundaries using a second fast marching analysis of gradient information of the image data inside a region of interest, which includes a finger joint of the at least one finger joint, determining the joint space width given extracted bone boundaries, and outputting the joint space width.