Siemens Medical Solutions USA, Inc.

Siemens Corporate Research, Inc.

Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Carol L Novak

Atilla Peter Kiraly

Benjamin L Odry

Jianzhong Qian

Bernhard Geiger

Li Zhang

Scott T Rickard, Jr

Thomas R Hancock

Russell E Gorr

Stephen J Judd

Zhenyu Wu

Lutz Gündel

Qing Xu

Hong Chen

Sreerama K Murthy

Bjoern Coenen

Sreerama K V Murthy

Martin S Glassman

Dorin Comaniciu

Puneet Sharma

Shaohua Kevin Zhou

Guo-Qing Wei

Li Fan

David D Liu

Saikiran Rapaka

Stefan Kluckner

Zhoubing Xu

Daguang Xu

Lutz Guendel

James G Reisman

Helmuth Schramm

Michael Sarju Vaz

Barak P Pearlmutter

J Stephen Judd

Long-Ji Lin

Chong Chen

Aaron Bobick

Jiancong Wang

Barak A Pearlmutter

David P Naidich

Jing Huo

Jeanne Verre

Jens N Kaftan

Eric Pichon

A method of evaluating airway wall density and inflammation including: segmenting a bronchial tree to create an airway wall map; for each branch, taking a set of locations that form the wall of each branch from the map and sampling the value in a virtual non-contrast image of the bronchial tree and, given a set of samples of pre-contrast densities, computing a value to yield a bronchial wall density for each branch to yield density measures; for each branch, taking the set of locations that form the wall of each branch from the map and sampling the value in a contrast agent map of the bronchial tree and, given the set of samples of contrast agent intake, computing a value to yield a bronchial wall uptake for each branch to yield inflammation measures; and using the density and inflammation measures to determine treatment or predict outcome for a patient.

Method and system for simultaneous evaluation of airway wall density and airway wall inflammation

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.

Visualization of lung fissures in medical imaging

A generative network is used for lung lobe segmentation or lung fissure localization, or for training a machine network for lobar segmentation or localization. For segmentation, deep learning is used to better deal with a sparse sampling of training data. To increase the amount of training data available, an image-to-image or generative network localizes fissures in at least some of the samples. The deep-learnt network, fissure localization, or other segmentation may benefit from generative localization of fissures.

Trained generative network for lung segmentation in medical imaging

A method for training a segmentation correction model includes performing an iterative model training process over a plurality of iterations. During each iteration, an initial segmentation estimate for an image is provided to a human annotators via an annotation interface. The initial segmentation estimate identifies one or more anatomical areas of interest within the image. Interactions with the annotation interface are automatically monitored to record annotation information comprising one or more of (i) segmentation corrections made to the initial segmentation estimate by the annotators via the annotation interface, and (ii) interactions with the annotation interface performed by the annotators while making the corrections. A base segmentation machine learning model is trained to automatically create a base segmentation based on the image. Additionally, a segmentation correction machine learning model is trained to automatically perform the segmentation corrections based on the image.

Highly integrated annotation and segmentation system for medical imaging

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.

Patient-specific radiation dose assessment in medical therapy

A method and apparatus for automated boundary delineation of a tubular structure in a 3D medical image of a patient using an infinitely recurrent neural network (IRNN) is disclosed. An unraveled cross-section image corresponding to a portion of a tubular structure is extracted from 3D medical image. The unraveled cross-section image is divided into a plurality of image chunks. A boundary of the portion of the tubular structure is detected based on the plurality of image chunks using a trained IRNN. The trained IRNN repeatedly inputs a sequential data stream, including the plurality of image chunks of the unraveled cross-section image, for a plurality of iterations while preserving a memory state between iterations, and detects, for each image chunk of the unraveled cross-section image input to the trained IRNN in the sequential data stream, a corresponding section of the boundary of the portion of the tubular structure.

Method and system for accurate boundary delineation of tubular structures in medical images using infinitely recurrent neural networks

A computer-implemented method for providing a multi-modality visualization of a patient includes receiving one or more image datasets. Each image dataset corresponds to a distinct image modality. The image datasets are segmented into a plurality of anatomical objects. A list of clinical tasks associated with displaying the one or more image datasets are received. A machine learning model is used to determine visualization parameters for each anatomical object based on the list of clinical tasks. Then, a synthetic display of the image datasets is created by presenting each anatomical object according to its corresponding visualization parameters.

System for synthetic display of multi-modality data

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.

Visualizing different types of airway wall abnormalities

A method for depicting an airway tree of a patient includes: (a) generating an iodine map of the airway tree from dual energy computed tomography (DECT) imaging data acquired from the patient; (b) defining a region of interest of the airway tree from the DECT imaging data; (c) rendering at least a portion of the airway tree based on information derived from the iodine map and the defined region of interest; and (d) displaying a graphical image of at least a portion of the airway tree on a user interface. Systems for depicting an airway tree of a patient are described.

Visualization of dual energy computed tomography airways data

Dynamic systems and methods for depicting context among different views of an imaging visualization application used by a medical workstation are provided.

Dynamic graphical user interfaces for medical workstations cross-reference to related applications

A method including displaying a three-dimensional (3D) image of a lung, receiving a selection of an airway of the lung and displaying a two-dimensional (2D) cross-section image of the airway perpendicular to the airway's long axis, wherein the display of the 2D cross-section image occurs almost immediately after the selection of the airway is received.

Automated detection of airway and vessel orientations for quantitative analysis and visualization

A dynamic method () to better depict context among the different views of an imaging visualization application used by a medical workstation.

Dynamic graphical user interfaces for medical workstations

A method () that evaluates the contours of patient airways and visualizes anatomical changes to the airways.

Method for visualization of airway anatomical changes

A method for labeling connected tubular objects within segmented image data, including: receiving segmented image data; and labeling the segmented image data to identify a plurality of components in the segmented image data, wherein the labeling includes: processing the segmented image data to create a processed image that represents centerline and radii estimates of the connected tubular components; determining seed point candidates in the processed image that are within a band of radii; grouping the candidates based on their physical distance from each other and their radii estimates; partitioning the segmented image data in accordance with the grouped candidates; and assigning a separate color label to each of the plurality of components that are different from each other.

Method for automatic separation of segmented tubular and circular objects

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.

System and method for grouping airways and arteries for quantitative analysis

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Newtown, PA, United States of America

Carol L Novak