Patents by Inventor Andrei Chekkoury

Andrei Chekkoury 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: 12190523
    Abstract: Systems and methods for determining a segmentation of a hemorrhage are provided. An input medical image of a hemorrhage of a patient is received. A contour-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based contour-sensitive segmentation network. A detection-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based detection-sensitive segmentation network. A final segmentation of the hemorrhage from the input medical image is determined based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation. The final segmentation of the hemorrhage is output.
    Type: Grant
    Filed: February 15, 2022
    Date of Patent: January 7, 2025
    Assignee: Siemens Healthineers AG
    Inventors: Youngjin Yoo, Eli Gibson, Bogdan Georgescu, Gengyan Zhao, Thomas Re, Jyotipriya Das, Eva Eibenberger, Andrei Chekkoury
  • Patent number: 12112844
    Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.
    Type: Grant
    Filed: March 12, 2021
    Date of Patent: October 8, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Eli Gibson, Bogdan Georgescu, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Thomas Re, Eva Eibenberger, Andrei Chekkoury, Barbara Brehm, Thomas Flohr, Dorin Comaniciu, Pierre-Hugo Trigan
  • Patent number: 11861828
    Abstract: Systems and methods for quantifying a shift of an anatomical object of a patient are provided. A 3D medical image of an anatomical object of a patient is received. An initial location of landmarks on the anatomical object in the 3D medical image is determined using a first machine learning network. A 2D slice depicting the initial location of the landmarks is extracted from the 3D medical image. The initial location of the landmarks in the 2D slice is refined using a second machine learning network. A shift of the anatomical object is quantified based on the refined location of the landmarks in the 2D slice. The quantified shift of the anatomical object is output.
    Type: Grant
    Filed: June 10, 2021
    Date of Patent: January 2, 2024
    Assignee: Siemens Healthcare GmbH
    Inventors: Nguyen Nguyen, Youngjin Yoo, Pascal Ceccaldi, Eli Gibson, Andrei Chekkoury
  • Patent number: 11861835
    Abstract: Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.
    Type: Grant
    Filed: March 25, 2021
    Date of Patent: January 2, 2024
    Assignee: Siemens Healthcare GmbH
    Inventors: Youngjin Yoo, Thomas Re, Eli Gibson, Andrei Chekkoury
  • Publication number: 20230316532
    Abstract: Systems and methods for determining a segmentation of a hemorrhage are provided. An input medical image of a hemorrhage of a patient is received. A contour-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based contour-sensitive segmentation network. A detection-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based detection-sensitive segmentation network. A final segmentation of the hemorrhage from the input medical image is determined based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation. The final segmentation of the hemorrhage is output.
    Type: Application
    Filed: February 15, 2022
    Publication date: October 5, 2023
    Inventors: Youngjin Yoo, Eli Gibson, Bogdan Georgescu, Gengyan Zhao, Thomas Re, Jyotipriya Das, Eva Eibenberger, Andrei Chekkoury
  • Publication number: 20220309667
    Abstract: Systems and methods for assessing expansion of an abnormality are provided. A first input medical image of a patient depicting an abnormality at a first time and a second input medical image of the patient depicting the abnormality at a second time are received. The second input medical image is registered with the first input medical image. The abnormality is segmented from 1) the first input medical image to generate a first segmentation map and 2) the registered second input medical image to generate a second segmentation map. The first segmentation map and the second segmentation map are combined to generate a combined map. Features are extracted from the first input medical image and the registered second input medical image are based on the combined map. Expansion of the abnormality is assessed based on the extracted features using a trained machine learning based network. Results of the assessment are output.
    Type: Application
    Filed: March 25, 2021
    Publication date: September 29, 2022
    Inventors: Youngjin Yoo, Thomas Re, Eli Gibson, Andrei Chekkoury
  • Publication number: 20220293247
    Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.
    Type: Application
    Filed: March 12, 2021
    Publication date: September 15, 2022
    Inventors: Eli Gibson, Bogdan Georgescu, Pascal Ceccaldi, Youngjin Yoo, Jyotipriya Das, Thomas Re, Eva Eibenberger, Andrei Chekkoury, Barbara Brehm, Thomas Flohr, Dorin Comaniciu, Pierre-Hugo Trigan
  • Publication number: 20220067929
    Abstract: Systems and methods for quantifying a shift of an anatomical object of a patient are provided. A 3D medical image of an anatomical object of a patient is received. An initial location of landmarks on the anatomical object in the 3D medical image is determined using a first machine learning network. A 2D slice depicting the initial location of the landmarks is extracted from the 3D medical image. The initial location of the landmarks in the 2D slice is refined using a second machine learning network. A shift of the anatomical object is quantified based on the refined location of the landmarks in the 2D slice. The quantified shift of the anatomical object is output.
    Type: Application
    Filed: June 10, 2021
    Publication date: March 3, 2022
    Inventors: Nguyen Nguyen, Youngjin Yoo, Pascal Ceccaldi, Eli Gibson, Andrei Chekkoury