Patents by Inventor Bogdan Georgescu

Bogdan Georgescu 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).

  • Publication number: 20240177454
    Abstract: Provided are computer-implemented methods and systems for classifying a medical image data set. In particular, a method is provided comprising the steps of receiving the medical image dataset of a patient, of providing a first classification stage configured to classify the medical image dataset as normal or not-normal, of providing a second classification stage different than the second classification stage and configured to classify the medical image dataset as normal or not-normal, and of subjecting the medical image dataset to the first classification stage to classify the medical image dataset as normal or not-normal. Further, the method comprises subjecting the medical image dataset to the second classification stage to classify the medical image dataset as normal or not-normal, if the medical image dataset is classified as normal in the first classification stage.
    Type: Application
    Filed: November 27, 2023
    Publication date: May 30, 2024
    Applicant: Siemens Healthcare GmbH
    Inventors: Awais MANSOOR, Ingo SCHMUECKING, Rikhiya GHOSH, Oladimeji FARRI, Jianing WANG, Bogdan GEORGESCU, Sasa GRBIC, Philipp HOELZER, Dorin COMANICIU
  • Publication number: 20240070853
    Abstract: Systems and methods for performing a medical imaging analysis task are provided. A plurality of 3D (three dimensional) patches extracted from a 3D input medical image is received. A set of local features is extracted from each of the plurality of 3D patches using a machine learning based local feature extractor network. Global features representing relationships between the sets of local features are determined. A medical imaging analysis task is performed on the 3D input medical image based on the global features. Results of the medical imaging analysis task are output.
    Type: Application
    Filed: August 23, 2022
    Publication date: February 29, 2024
    Inventors: Youngjin Yoo, Eli Gibson, Gengyan Zhao, Bogdan Georgescu
  • Publication number: 20230404512
    Abstract: Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.
    Type: Application
    Filed: June 20, 2022
    Publication date: December 21, 2023
    Inventors: Bogdan Georgescu, Eli Gibson, Thomas Re, Dorin Comaniciu
  • Publication number: 20230368383
    Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
    Type: Application
    Filed: July 13, 2023
    Publication date: November 16, 2023
    Inventors: Rui Liao, Shun Miao, Pierre de Tournemire, Julian Krebs, Li Zhang, Bogdan Georgescu, Sasa Grbic, Florin Cristian Ghesu, Vivek Kumar Singh, Daguang Xu, Tommaso Mansi, Ali Kamen, Dorin Comaniciu
  • Patent number: 11810291
    Abstract: Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.
    Type: Grant
    Filed: May 1, 2020
    Date of Patent: November 7, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Siqi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastien Piat, Brian Teixeira, Thomas Re, Dorin Comaniciu
  • Publication number: 20230351601
    Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.
    Type: Application
    Filed: July 10, 2023
    Publication date: November 2, 2023
    Applicant: Siemens Healthcare GmbH
    Inventors: Siqi LIU, Yuemeng LI, Arnaud Arindra ADIYOSO, Bogdan GEORGESCU, Sasa GRBIC, Ziming QIU, Zhengyang SHEN
  • 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
  • Patent number: 11776117
    Abstract: For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.
    Type: Grant
    Filed: October 16, 2020
    Date of Patent: October 3, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Sebastian Guendel, Arnaud Arindra Adiyoso, Florin-Cristian Ghesu, Sasa Grbic, Bogdan Georgescu, Dorin Comaniciu
  • Patent number: 11748886
    Abstract: A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.
    Type: Grant
    Filed: April 13, 2021
    Date of Patent: September 5, 2023
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Siqi Liu, Yuemeng Li, Arnaud Arindra Adiyoso, Bogdan Georgescu, Sasa Grbic, Ziming Qiu, Zhengyang Shen
  • Patent number: 11741605
    Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
    Type: Grant
    Filed: December 12, 2022
    Date of Patent: August 29, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Rui Liao, Shun Miao, Pierre de Tournemire, Julian Krebs, Li Zhang, Bogdan Georgescu, Sasa Grbic, Florin Cristian Ghesu, Vivek Kumar Singh, Daguang Xu, Tommaso Mansi, Ali Kamen, Dorin Comaniciu
  • Publication number: 20230238094
    Abstract: A trained ML algorithm may be configured to process medical imaging data to generate a prediction of at least one diagnosis of a patient based on the medical imaging data. The prediction of the at least one diagnosis of the patient is compared with a validated label of the at least one diagnosis of the patient and the performance of the trained ML algorithm is determined based on the comparison. The validated label of the at least one diagnosis of the patient is obtained by parsing a validated radiology report of the patient and the medical imaging data is associated with the validated radiology report. If the performance of the trained ML algorithm is lower than a threshold, an update of parameters of the trained ML algorithm may be triggered based on the validated label.
    Type: Application
    Filed: January 9, 2023
    Publication date: July 27, 2023
    Applicant: Siemens Healthcare GmbH
    Inventors: Andrei CHEKKOURY, Eva Eibenberger, Eli Gibson, Bogdan Georgescu, Grzegorz Soza, Michael Suehling, Dorin Comaniciu
  • Publication number: 20230154164
    Abstract: Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.
    Type: Application
    Filed: November 12, 2021
    Publication date: May 18, 2023
    Inventors: Florin-Cristian Ghesu, Bogdan Georgescu, Awais Mansoor, Sasa Grbic, Dorin Comaniciu
  • Publication number: 20230114934
    Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
    Type: Application
    Filed: December 12, 2022
    Publication date: April 13, 2023
    Inventors: Rui Liao, Shun Miao, Pierre de Tournemire, Julian Krebs, Li Zhang, Bogdan Georgescu, Sasa Grbic, Florin Cristian Ghesu, Vivek Kumar Singh, Daguang Xu, Tommaso Mansi, Ali Kamen, Dorin Comaniciu
  • Publication number: 20230099938
    Abstract: Systems and methods for determining input data is out-of-domain of an AI (artificial intelligence) based system are provided. Input data for inputting into an AI based system is received. An in-domain feature space of the AI based system and an out-of-domain feature space of the AI based system are modelled. The in-domain feature space corresponds to features of data that the AI based system is trained to classify. The out-of-domain feature space corresponds to features of data that the AI based system is not trained to classify. Probability distribution functions in the in-domain feature space and the out-of-domain feature space are generated for the input data and for the data that the AI based system is trained to classify. It is determined whether the input data is out-of-domain of the AI based system based on the probability distribution functions for the input data and for the data that the AI based system is trained to classify.
    Type: Application
    Filed: September 29, 2021
    Publication date: March 30, 2023
    Inventors: Bogdan Georgescu, Eli Gibson, Florin-Cristian Ghesu, Dorin Comaniciu, Athira Jane Jacob, Tiziano Passerini, Puneet Sharma
  • Publication number: 20230102246
    Abstract: Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.
    Type: Application
    Filed: September 29, 2021
    Publication date: March 30, 2023
    Inventors: Bogdan Georgescu, Eli Gibson, Thomas Re, Dorin Comaniciu, Florin-Cristian Ghesu, Vivek Singh
  • Publication number: 20230091197
    Abstract: The disclosure provides a computer-implemented method for detecting a failure of a device, wherein the device is connected to a sensor, the method comprising: receiving, by a machine learning model, a trace signal from the sensor indicating a status of the device; encoding, by the machine learning model, the trace signal into a plurality of vector representations; and determining, by the machine learning model, whether the trace signal is valid or invalid based on the plurality of vector representations.
    Type: Application
    Filed: February 25, 2021
    Publication date: March 23, 2023
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Mark Edwards, Vivek Singh, Bogdan Georgescu, Ankur Kapoor
  • Publication number: 20230057653
    Abstract: Systems and methods for providing a means for improving the expressiveness and/or robustness of a machine learning system's result, based on imaging data and/or to make it possible to combine imaging data with non-imaging data to improve statements, which are deduced from the imaging data. The object is achieved by a computer implemented method, and uncertainty quantifier, medical system and a computer program product, and includes receiving a set of input data quantified as uncertainty, providing an information fusion algorithm, and applying the received set of input data on the provided information fusion algorithm, while modeling the propagation of uncertainty through the information fusion algorithm to predict an uncertainty for the medical assessment as a result (r), provided by the machine-learning system (M), based on the provided set of input data.
    Type: Application
    Filed: August 12, 2022
    Publication date: February 23, 2023
    Inventors: Florin-Cristian Ghesu, Awais Mansoor, Sasa Grbic, Ramya Vunikili, Sanjeev Kumar Karn, Rajeev Bhatt Ambati, Oladimeji Farri, Bogdan Georgescu, Dorin Comaniciu
  • Patent number: 11557036
    Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.
    Type: Grant
    Filed: April 29, 2020
    Date of Patent: January 17, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Rui Liao, Shun Miao, Pierre de Tournemire, Julian Krebs, Li Zhang, Bogdan Georgescu, Sasa Grbic, Florin Cristian Ghesu, Vivek Kumar Singh, Daguang Xu, Tommaso Mansi, Ali Kamen, Dorin Comaniciu
  • Patent number: 11514571
    Abstract: Systems and methods for identifying and assessing lymph nodes are provided. Medical image data (e.g., one or more computed tomography images) of a patient is received and anatomical landmarks in the medical image data are detected. Anatomical objects are segmented from the medical image data based on the one or more detected anatomical landmarks. Lymph nodes are identified in the medical image data based on the one or more detected anatomical landmarks and the one or more segmented anatomical objects. The identified lymph nodes may be assessed by segmenting the identified lymph nodes from the medical image data and quantifying the segmented lymph nodes. The identified lymph nodes and/or the assessment of the identified lymph nodes are output.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: November 29, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Elijah D. Bolluyt, Alexandra Comaniciu, Sasa Grbic
  • 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