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).

  • Publication number: 20250111656
    Abstract: Systems and methods for performing a medical analysis task using a trained machine learning based task network are provided. Input medical data is received. A medical analysis task is performed using a trained machine learning based task network based on the input medical data. Results of the medical analysis task are output. The trained machine learning based task network is trained by: receiving unannotated training medical data; generating weakly-supervised labels for the unannotated training medical data using one or more trained machine learning based supervised learning networks; training the machine learning based task network for performing the medical analysis task based on 1) the unannotated training medical data, 2) self-supervised labels for the unannotated training medical data learned via self-supervised learning, and 3) the generated weakly-supervised labels for the unannotated training medical data; and outputting the trained machine learning based task network.
    Type: Application
    Filed: September 28, 2023
    Publication date: April 3, 2025
    Inventors: Venkatesh Narasimha Murthy, Bogdan Georgescu, Florin-Cristian Ghesu, Mehmet Akif Gulsun, Dominik Neumann, Alexandru Constantin Serban, Dorin Comaniciu
  • Publication number: 20250107756
    Abstract: Rather than the whole-body scout scan, multiple scout scans of different regions are used to locate the target for magnetic resonance scanning. A diagnostic scan may be planned in as little time as possible based on localization using multiple scout scans of different regions of the patient. The planning of the different regions may be optimized to minimize the number and/or time for localizing the target.
    Type: Application
    Filed: September 28, 2023
    Publication date: April 3, 2025
    Inventors: Boris Mailhe, Stefan Popescu, Dorin Comaniciu
  • Publication number: 20250104276
    Abstract: Systems and methods for performing a medical imaging analysis task based on pixelwise positionally encoded features are provided. One or more input medical images are received. One or more pixelwise positional embedding images are generated for the one or more input medical images using a spatially varying function. Patches are extracted from the one or more input medical images and the one or more pixelwise positional embedding images. The patches extracted from the one or more input medical images are encoded with corresponding ones of the patches extracted from the one or more pixelwise positional embedding images into pixelwise positionally encoded features. A medical imaging analysis task is performed using a machine learning based network based on the pixelwise positionally encoded features. Results of the medical imaging analysis task are output.
    Type: Application
    Filed: September 26, 2023
    Publication date: March 27, 2025
    Inventors: Gengyan Zhao, Badhan Kumar Das, Eli Gibson, Dorin Comaniciu
  • Publication number: 20250093448
    Abstract: For artificial Intelligence-based optimization of a MR sequence, an agent machine trained with reinforcement learning generates the MR pulse sequence for a patient. The agent may generate values for multiple or all the parameters defining the MR pulse sequence. The agent was trained using the end goal or task (e.g., MR map or segmentation) as the reward function, so the MR pulse sequence generated by the agent provides good quality MR imaging. The agent generates the MR pulse sequence quickly and without requiring multiple sequences to be used on the patient.
    Type: Application
    Filed: September 18, 2023
    Publication date: March 20, 2025
    Inventors: Simon Arberet, Dorin Comaniciu
  • Publication number: 20250095849
    Abstract: Systems and methods for managing patient diagnostic and therapy workflows in a hospital and/or radiology centers. A radiology recommendation agent is trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time.
    Type: Application
    Filed: February 27, 2024
    Publication date: March 20, 2025
    Inventors: Vivek Singh, Ankur Kapoor, Ingo Schmuecking, Scott Steingall, David Scholl, Dorin Comaniciu
  • Publication number: 20250078471
    Abstract: Systems and methods for assigning labels to medical images are provided. One or more prompts comprising instructions for assigning labels to medical images are received. The labels are assigned to the medical images using a large language model based on 1) the instructions and 2) patient data stored in a plurality of patient databases. The assignment of the labels to the medical images is output.
    Type: Application
    Filed: August 28, 2023
    Publication date: March 6, 2025
    Inventors: Sasa Grbic, Dorin Comaniciu
  • Publication number: 20250079011
    Abstract: Systems and methods for performing one or more medical analysis tasks are provided. Patient data of a patient is received for a set of biomarkers acquired at one or more time points within a period of time. The patient data is encoded using an encoder network to generate patient data embeddings. One or more medical analysis tasks are performed based on the patient data embeddings using one or more decoder networks. The one or more medical analysis tasks comprise generating recommendations for a clinical course of action. Results of the one or more medical analysis tasks are output.
    Type: Application
    Filed: September 1, 2023
    Publication date: March 6, 2025
    Inventors: Vivek Singh, Ali Kamen, Raj Gopalan, Jamie Gramz, Dorin Comaniciu
  • Publication number: 20250068668
    Abstract: Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.
    Type: Application
    Filed: August 22, 2023
    Publication date: February 27, 2025
    Inventors: Sasa Grbic, Eli Gibson, Oladimeji Farri, Bogdan Georgescu, Gianluca Paladini, Puneet Sharma, Daphne Yu, Dorin Comaniciu
  • Publication number: 20250054603
    Abstract: Systems and methods for performing one or more medical imaging analysis tasks are provided. A sequence of medical images is received. One or more patches are extracted from each image of the sequence of medical images. Spatio-temporal features are extracted from the one or more extracted patches using a machine learning based encoder network. One or more medical imaging analysis tasks are performed based on the extracted spatio-temporal features. Results of the one or more medical imaging analysis tasks are output. The machine learning based encoder network is trained by receiving a sequence of training medical images. Patches of a first set of images of the sequence of training medical images are masked according to a first masking strategy. Patches of a second set of images of the sequence of training medical images are masked according to a second masking strategy.
    Type: Application
    Filed: January 23, 2024
    Publication date: February 13, 2025
    Inventors: Dominik Neumann, Saahil Islam, Venkatesh Narasimha Murthy, Serkan Cimen, Florin-Cristian Ghesu, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20250044390
    Abstract: Systems and methods for AI-powered histological fingerprinting in magnetic resonance imaging. MR signal data of an object is acquired using a high sensitivity scanner. Ground truth tissue microstructure data is acquired for the object. A forward model is learned using machine learning. The forward model is used to generate a dictionary or to train a model to map the signals to the histological parameters including the tissue microstructure of a scanner object. A signal-to-signal translation model is also provided to provide signals with improved sensitivity.
    Type: Application
    Filed: August 3, 2023
    Publication date: February 6, 2025
    Inventors: Mahmoud Mostapha, Dorin Comaniciu, Mariappan S. Nadar
  • Publication number: 20250046434
    Abstract: Systems and methods for determining an uncertainty measure associated with results of a medical task performed by an LLM (large language model) are provided. One or more prompts associated with a medical task are received. At least one of the one or more prompts are encoded into a set of features using a feature encoder network of an LLM. The medical task is performed based on the set of features using a decoder network of the LLM. An uncertainty measure associated with results of the medical task is determined based on the set of features using an uncertainty quantification module of the LLM. The results of the medical task and the uncertainty measure are output.
    Type: Application
    Filed: August 1, 2023
    Publication date: February 6, 2025
    Inventors: Bogdan Georgescu, Oladimeji Farri, Dorin Comaniciu
  • Publication number: 20250045912
    Abstract: A system for synthesizing medical images including synthesizing medical abnormalities has multiple diffusion model based denoising stages. At a first denoising stage, a machine-learned network denoises a first noise input to obtain an abnormality spatial mask detailing positional and structural characteristics of the synthesized medical abnormality. At a second denoising stage, a machine-learned network denoises a second noise input based on the abnormality spatial mask and a pre-abnormality image to obtain a synthesized medical image that corresponds to the pre-abnormality image with the synthesized medical abnormality inserted consistent with the abnormality spatial mask.
    Type: Application
    Filed: August 4, 2023
    Publication date: February 6, 2025
    Inventors: Gengyan Zhao, Eli Gibson, Boris Mailhe, Youngjin Yoo, Bogdan Georgescu, Dorin Comaniciu
  • Patent number: 12182998
    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: Grant
    Filed: February 25, 2022
    Date of Patent: December 31, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Simon Arberet, Mariappan S. Nadar, Mahmoud Mostapha, Dorin Comaniciu
  • Publication number: 20240423575
    Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.
    Type: Application
    Filed: September 5, 2024
    Publication date: December 26, 2024
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
  • Publication number: 20240428401
    Abstract: Systems and methods for performing an assessment of one or more tumors are provided. A plurality of input medical images of a patient acquired at a plurality of points in time is received. One or more tumors are identified in each of the plurality of input medical images. A tumor burden of the patient is determined for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks. An assessment of the one or more tumors is performed based on the tumor burden of the patient determined for each of the plurality of points in time. Results of the assessment of the one or more tumors are output.
    Type: Application
    Filed: June 22, 2023
    Publication date: December 26, 2024
    Inventors: Bin Lou, Julian Rosenman, Patrick Kupelian, Zhoubing Xu, Sasa Grbic, Ali Kamen, Dorin Comaniciu
  • Publication number: 20240394883
    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: August 8, 2024
    Publication date: November 28, 2024
    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: 12112470
    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: Grant
    Filed: September 29, 2021
    Date of Patent: October 8, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Bogdan Georgescu, Eli Gibson, Thomas Re, Dorin Comaniciu, Florin-Cristian Ghesu, Vivek Singh
  • Patent number: 12109061
    Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.
    Type: Grant
    Filed: March 9, 2021
    Date of Patent: October 8, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
  • 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: 12102423
    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: Grant
    Filed: June 16, 2022
    Date of Patent: October 1, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Boris Mailhe, Dorin Comaniciu, Ali Kamen, Mariappan S. Nadar, Bin Lou, Andreas Greiser, Venkata Veerendranadh Chebrolu