Patents by Inventor Dorin Comaniciu

Dorin Comaniciu 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: 11830606
    Abstract: Systems and methods for predicting risk for a medical event associated with evaluating or treating a patient for a disease are provided. Input medical imaging data and patient data of a patient are received. The input medical imaging data includes abnormality patterns associated with a disease. Imaging features are extracted from the input medical imaging data using a trained machine learning based feature extraction network. One or more of the extracted imaging features are normalized. The one or more normalized extracted imaging features and the patient data are encoded into features using a trained machine learning based encoder network. Risk for a medical event associated with evaluating or treating the patient for the disease is predicted based on the encoded features.
    Type: Grant
    Filed: June 3, 2020
    Date of Patent: November 28, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Ingo Schmuecking, Sasa Grbic, 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: 20230342933
    Abstract: For prediction of response of radiation therapy, radiomics are used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data and from segmentation. The trained encoder is then used to generate latent representations to be used as input to different classifiers or regressors for prediction of therapy responses, such as one classifier to predict response for an organ at risk and another classifier to predict another type of response for the organ at risk or to predict a response for the tumor.
    Type: Application
    Filed: April 22, 2022
    Publication date: October 26, 2023
    Inventors: Bin Lou, Zhoubing Xu, Ali Kamen, Sasa Grbic, Dorin Comaniciu
  • 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
  • Publication number: 20230274418
    Abstract: For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.
    Type: Application
    Filed: February 25, 2022
    Publication date: August 31, 2023
    Inventors: Simon Arberet, Mariappan S. Nadar, Mahmoud Mostapha, Dorin Comaniciu
  • 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: 20230252623
    Abstract: Systems and methods for performing a quality assessment of a medical imaging analysis task are provided. At least one low-field MRI (magnetic resonance imaging) quality assurance imaging data of the patient is received. A quality assessment of a medical imaging analysis task is performed based on the at least one low-field MRI quality assurance imaging data using one or more machine learning based networks. Results of the quality assessment are output.
    Type: Application
    Filed: December 8, 2022
    Publication date: August 10, 2023
    Inventors: Bin Lou, Ali Kamen, Boris Mailhe, Mariappan S. Nadar, Dorin Comaniciu
  • Publication number: 20230253095
    Abstract: For data analytics in magnetic resonance (MR) scanning, the scanning configuration information and the resulting raw data are directly used to determine the analytics or clinical decision. Artificial intelligence provides a value for a clinical finding characteristic of the patient based on the raw data from scanning and the controls used to scan, allowing the value to be based on all of the information content of the scan results. Reconstruction is not needed, allowing for simpler hardware, such as hardware with less homogeneous B0 and/or B1 fields than the norm and/or non-linear gradients.
    Type: Application
    Filed: May 31, 2022
    Publication date: August 10, 2023
    Inventors: Boris Mailhe, Dorin Comaniciu, Ali Kamen, Bin Lou, Mariappan S. Nadar, Andreas Greiser, Venkata Veerendranadh Chebrolu
  • Publication number: 20230253117
    Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A knowledge graph is computed based on the input medical data. A vector representing a state of the patient is generated based on the knowledge graph. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.
    Type: Application
    Filed: August 4, 2021
    Publication date: August 10, 2023
    Inventors: Vivek Singh, Matthias Siebert, Ali Kamen, Puneet Sharma, Ankur Kapoor, Dorin Comaniciu
  • Publication number: 20230253116
    Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A vector representing a state of the patient is generated based on the input medical data. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.
    Type: Application
    Filed: August 4, 2021
    Publication date: August 10, 2023
    Inventors: Vivek Singh, Matthias Siebert, Ali Kamen, Puneet Sharma, Ankur Kapoor, Dorin Comaniciu
  • Publication number: 20230248255
    Abstract: For autonomous MR scanning for a given medical test, a simplified MR scanner may be used without or will little input or control by a technologist (e.g., by a physician, radiologist, or person trained in MR scanner operation). The MR scanner autonomously positions, scans, checks quality, analyzes, and/or outputs an answer to a diagnostic question with or without an MR image. Scan analysis, based on artificial intelligence, allows for on-going or on-the-fly alteration of the scanning configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both position of the patient relative to the MR scanner and localization of the scan by the MR scanner are jointly solved. Sensors may sense a patient in a scan position where the reduced radio frequency requirements allow for a more open bore.
    Type: Application
    Filed: June 16, 2022
    Publication date: August 10, 2023
    Inventors: Boris Mailhe, Dorin Comaniciu, Ali Kamen, Mariappan S. Nadar, Bin Lou, Andreas Greiser, Venkata Veerendranadh Chebrolu
  • Patent number: 11717233
    Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.
    Type: Grant
    Filed: July 21, 2020
    Date of Patent: August 8, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Florin-Cristian Ghesu, Siqi Liu, Awais Mansoor, Sasa Grbic, Sebastian Vogt, Dorin Comaniciu, Ruhan Sa, Zhoubing Xu
  • 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
  • Patent number: 11710566
    Abstract: Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.
    Type: Grant
    Filed: June 7, 2019
    Date of Patent: July 25, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Dorin Comaniciu
  • Publication number: 20230221392
    Abstract: For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.
    Type: Application
    Filed: January 11, 2022
    Publication date: July 13, 2023
    Inventors: Boris Mailhe, Dorin Comaniciu, Simon Arberet, Nirmal Janardhanan, Mariappan S. Nadar, Hongki Lim, Mahmoud Mostapha
  • Publication number: 20230165638
    Abstract: Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions.
    Type: Application
    Filed: November 29, 2021
    Publication date: June 1, 2023
    Inventors: Tommaso Mansi, Young-Ho Kim, Rui Liao, Yue Zhang, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20230157761
    Abstract: Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.
    Type: Application
    Filed: November 24, 2021
    Publication date: May 25, 2023
    Inventors: Rui Liao, Young-Ho Kim, Jarrod Collins, Abdoul Aziz Amadou, Sebastien Piat, Ankur Kapoor, Tommaso Mansi, Noha El-Zehiry, Sasa Grbic, Dorin Comaniciu, Xianjun S. Zheng, Bo Liu, Zhoubing Xu, Jin-hyeong Park
  • 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