Patents by Inventor Carol L. Novak

Carol L. Novak has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10806372
    Abstract: 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.
    Type: Grant
    Filed: August 31, 2015
    Date of Patent: October 20, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Carol L. Novak, Benjamin L. Odry, Atilla Peter Kiraly
  • Patent number: 10796481
    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.
    Type: Grant
    Filed: April 20, 2018
    Date of Patent: October 6, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Lutz Guendel, Atilla Kiraly, Bernhard Geiger, Carol L. Novak
  • Patent number: 10607114
    Abstract: 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.
    Type: Grant
    Filed: January 16, 2018
    Date of Patent: March 31, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Carol L. Novak, Benjamin L. Odry, Atilla Peter Kiraly, Jiancong Wang
  • Publication number: 20190325645
    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.
    Type: Application
    Filed: April 20, 2018
    Publication date: October 24, 2019
    Inventors: Lutz Guendel, Atilla Kiraly, Bernhard Geiger, Carol L. Novak
  • Patent number: 10366490
    Abstract: 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.
    Type: Grant
    Filed: March 27, 2017
    Date of Patent: July 30, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Zhoubing Xu, Carol L. Novak, Atilla Peter Kiraly
  • Publication number: 20190220701
    Abstract: 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.
    Type: Application
    Filed: January 16, 2018
    Publication date: July 18, 2019
    Inventors: Carol L. Novak, Benjamin L. Odry, Atilla Peter Kiraly, Jiancong Wang
  • Patent number: 10258304
    Abstract: 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.
    Type: Grant
    Filed: November 29, 2017
    Date of Patent: April 16, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Patent number: 10188361
    Abstract: 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.
    Type: Grant
    Filed: March 27, 2017
    Date of Patent: January 29, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Bernhard Geiger, Shaohua Kevin Zhou, Carol L. Novak, Daguang Xu, David Liu
  • Publication number: 20180276815
    Abstract: 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.
    Type: Application
    Filed: March 27, 2017
    Publication date: September 27, 2018
    Inventors: Zhoubing Xu, Carol L. Novak, Atilla Peter Kiraly
  • Publication number: 20180271460
    Abstract: 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.
    Type: Application
    Filed: March 27, 2017
    Publication date: September 27, 2018
    Inventors: Bernhard Geiger, Shaohua Kevin Zhou, Carol L. Novak, Daguang Xu, David Liu
  • Patent number: 9603576
    Abstract: 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.
    Type: Grant
    Filed: September 12, 2014
    Date of Patent: March 28, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Atilla Peter Kiraly, Benjamin L. Odry, Carol L. Novak
  • Publication number: 20170055876
    Abstract: 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.
    Type: Application
    Filed: August 31, 2015
    Publication date: March 2, 2017
    Inventors: Carol L. Novak, Benjamin L. Odry, Atilla Peter Kiraly
  • Patent number: 9552672
    Abstract: Dynamic systems and methods for depicting context among different views of an imaging visualization application used by a medical workstation are provided.
    Type: Grant
    Filed: September 24, 2014
    Date of Patent: January 24, 2017
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Patent number: 9286719
    Abstract: 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.
    Type: Grant
    Filed: August 8, 2011
    Date of Patent: March 15, 2016
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Publication number: 20150086099
    Abstract: 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.
    Type: Application
    Filed: September 12, 2014
    Publication date: March 26, 2015
    Inventors: Atilla Peter Kiraly, Benjamin L. Odry, Carol L. Novak
  • Publication number: 20150009213
    Abstract: Dynamic systems and methods for depicting context among different views of an imaging visualization application used by a medical workstation are provided.
    Type: Application
    Filed: September 24, 2014
    Publication date: January 8, 2015
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Patent number: 8922546
    Abstract: A dynamic method (100) to better depict context among the different views of an imaging visualization application used by a medical workstation.
    Type: Grant
    Filed: September 7, 2011
    Date of Patent: December 30, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Patent number: 8675941
    Abstract: A method (100) that evaluates the contours of patient airways and visualizes anatomical changes to the airways.
    Type: Grant
    Filed: August 22, 2011
    Date of Patent: March 18, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Atilla Peter Kiraly, Carol L. Novak, Benjamin L. Odry
  • Patent number: 8532356
    Abstract: 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.
    Type: Grant
    Filed: June 14, 2007
    Date of Patent: September 10, 2013
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Atilla P. Kiraly, Carol L. Novak
  • Patent number: 8422748
    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.
    Type: Grant
    Filed: September 6, 2006
    Date of Patent: April 16, 2013
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Benjamin Odry, Atilla Peter Kiraly, Carol L. Novak