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: 11515030
    Abstract: An artificial agent based cognitive operating room system and a method thereof providing automated assistance for a surgical procedure are disclosed. Data related to the surgical procedure from multiple data sources is fused based on a current context. The data includes medical images of a patient acquired using one or more medical imaging modalities. Real-time quantification of patient measurements based on the data from the multiple data sources is performed based on the current context. Short-term predictions in the surgical procedure are forecasted based on the current context, the fused data, and the real-time quantification of the patient measurements. Suggestions for next steps in the surgical procedure and relevant information in the fused data are determined based on the current context and the short-term predictions. The suggestions for the next steps and the relevant information in the fused data are presented to an operator.
    Type: Grant
    Filed: June 23, 2017
    Date of Patent: November 29, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Tommaso Mansi, Ankur Kapoor, Thomas Pheiffer, Vincent Ordy, Dorin Comaniciu
  • Publication number: 20220358648
    Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.
    Type: Application
    Filed: June 28, 2022
    Publication date: November 10, 2022
    Inventors: Ali Kamen, Tongbai Meng, Mamadou Diallo, Bin Lou, Xin Yu, David Jean Winkel, Dorin Comaniciu, Robert Grimm, Berthold Kiefer, Heinrich von Busch
  • Publication number: 20220346742
    Abstract: CT scan parameters for performing a CT scan of an anatomical target region of a patient are determined and/or adjusted. An initial set of the CT scan parameters for starting to perform the CT scan is determined based on an initial set of attenuation curves associated with the anatomical target region of the patient. The initial set of attenuation curves are determined based on optical imaging data depicting the patient.
    Type: Application
    Filed: March 30, 2022
    Publication date: November 3, 2022
    Inventors: Brian Teixeira, Vivek Singh, Ankur Kapoor, Andreas Prokein, Dorin Comaniciu
  • Patent number: 11450431
    Abstract: A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.
    Type: Grant
    Filed: November 15, 2013
    Date of Patent: September 20, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Ali Kamen, Maneesh Kumar Singh, Sebastian Poelsterl, Lance Anthony Ladic, Dorin Comaniciu
  • 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: 20220292742
    Abstract: Systems and methods for generating a synthetic image are provided. An input medical image in a first modality is received. A synthetic image in a second modality is generated from the input medical image. The synthetic image is upsampled to increase a resolution of the synthetic image. An output image is generated to simulate image processing of the upsampled synthetic image. The output image is output.
    Type: Application
    Filed: March 11, 2021
    Publication date: September 15, 2022
    Inventors: Boris Mailhe, Florin-Cristian Ghesu, Siqi Liu, Sasa Grbic, Sebastian Vogt, Dorin Comaniciu, Awais Mansoor, Sebastien Piat, Steffen Kappler, Ludwig Ritschl
  • Patent number: 11443201
    Abstract: For machine learning for a medical imager, results created for individual patients are used to generate the ground truth. The acceptance or change for examining an individual patient is used as the ground truth instead of using a further expert study for the purposes of machine training. In this way, the medical imager creates both samples and ground truth as part of every-day, on-going examinations of patients in the production environment. Machine training is performed based on these samples, and the machine-learned network may then be applied for imaging further patients. For example, the medical imager self-optimizes or self-learns, allowing for updating the machine-learned network more rapidly (e.g., keeping pace with changes in practice in a lower cost and less time-consuming approach and/or updating localized to a practice) in the production environment.
    Type: Grant
    Filed: May 21, 2018
    Date of Patent: September 13, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Dorin Comaniciu
  • Patent number: 11430121
    Abstract: Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.
    Type: Grant
    Filed: April 1, 2020
    Date of Patent: August 30, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Shikha Chaganti, Sasa Grbic, Bogdan Georgescu, Zhoubing Xu, Siqi Liu, Youngjin Yoo, Thomas Re, Guillaume Chabin, Thomas Flohr, Valentin Ziebandt, Dorin Comaniciu, Brian Teixeira, Sebastien Piat
  • Patent number: 11403750
    Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.
    Type: Grant
    Filed: June 13, 2019
    Date of Patent: August 2, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Ali Kamen, Ahmet Tuysuzoglu, Bin Lou, Bibo Shi, Nicolas Von Roden, Kareem Abdelrahman, Berthold Kiefer, Robert Grimm, Heinrich von Busch, Mamadou Diallo, Tongbai Meng, Dorin Comaniciu, David Jean Winkel, Xin Yu
  • Patent number: 11398304
    Abstract: Since the final output for medical imaging is the radiology report, the quality of which is largely dependent on the radiologist, there is a need for a comprehensive system for both medical imaging and reporting. Imaging and radiology reporting are combined. Image acquisition, reading of the images, and reporting are linked, allowing feedback of readings to control acquisition so that the final reporting is more comprehensive. Clinical findings typically associated with reporting may be used automatically to feedback for further or continuing acquisition without requiring a radiologist. A clinical identification may be used to determine what image processing to perform for reading, and/or raw (i.e., non-reconstructed) scan data from the imaging system are provided for integrated image processing with report generation.
    Type: Grant
    Filed: April 24, 2018
    Date of Patent: July 26, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Dorin Comaniciu
  • Patent number: 11380084
    Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.
    Type: Grant
    Filed: February 11, 2020
    Date of Patent: July 5, 2022
    Inventors: Ali Kamen, Shanhui Sun, Terrence Chen, Tommaso Mansi, Alexander Michael Gigler, Patra Charalampaki, Maximilian Fleischer, Dorin Comaniciu
  • Publication number: 20220199254
    Abstract: Systems and methods for automatically determining an assessment of a patient are provided. A patient is automatically interacted with, by a first trained machine learning based model, to acquire initial patient data. One or more risk factors associated with the patient are automatically determined, by a second trained machine learning based model, based on the received initial patient data. The patient is automatically interacted with, by the first trained machine learning based model, to acquire additional patient data based on the one or more determined risk factors. An assessment of the patient is automatically determined, by the second trained machine learning based model, based on the initial patient data and the additional patient data. The assessment of the patient is output.
    Type: Application
    Filed: December 18, 2020
    Publication date: June 23, 2022
    Inventors: Ahmet Tuysuzoglu, Dorin Comaniciu, Tommaso Mansi
  • Patent number: 11342080
    Abstract: A method and system for automated disease progression modeling and therapy optimization for an individual patient is disclosed. A current condition of the patient is modeled using a state-variable model in which a plurality of state variables in a state vector represent a plurality of characteristics of the patient. Disease progression for the patient is predicted based on the state variables of the patient. An optimization is performed to determine an optimal therapy type and an optimal therapy timing for the patient based on the predicted disease progression for the patient.
    Type: Grant
    Filed: August 17, 2016
    Date of Patent: May 24, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Dorin Comaniciu
  • Patent number: 11334791
    Abstract: A trained recurrent neural network having a set of control policies learned from application of a template dataset and one or more corresponding template deep network architectures may generate a deep network architecture for performing a task on an application dataset. The template deep network architectures may have an established level or performance in executing the task. A deep network based on the deep network architecture may trained to perform the task on the application dataset. The control policies of the recurrent neural network may be updated based on the performance of the trained deep network.
    Type: Grant
    Filed: September 5, 2018
    Date of Patent: May 17, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Vivek Kumar Singh, Terrence Chen, Dorin Comaniciu
  • Patent number: 11308611
    Abstract: Systems and methods for reducing false positive detections of malignant lesions are provided. A candidate malignant lesion is detected in one or more medical images, such as, e.g., multi-parametric magnetic resonance images. One or more patches associated with the candidate malignant lesion are extracted from the one or more medical images. The candidate malignant lesion is classified as being a true positive detection of a malignant lesion or a false positive detection of the malignant lesion based on the one or more extract patches using a trained machine learning network. The results of the classification are output.
    Type: Grant
    Filed: February 5, 2020
    Date of Patent: April 19, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Xin Yu, Bin Lou, Bibo Shi, David Jean Winkel, Ali Kamen, Dorin Comaniciu
  • Patent number: 11304665
    Abstract: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.
    Type: Grant
    Filed: October 16, 2018
    Date of Patent: April 19, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Saikiran Rapaka, Xudong Zheng, Ali Kamen, Lucian Mihai Itu, Bogdan Georgescu, Dorin Comaniciu, Thomas Redel, Jan Boese, Viorel Mihalef
  • Publication number: 20220101987
    Abstract: A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.
    Type: Application
    Filed: September 1, 2021
    Publication date: March 31, 2022
    Inventors: Ahmet Tuysuzoglu, Eli Gibson, Dorin Comaniciu
  • Publication number: 20220079552
    Abstract: For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.
    Type: Application
    Filed: November 22, 2021
    Publication date: March 17, 2022
    Inventors: Huseyin Tek, Bogdan Georgescu, Tommaso Mansi, Frank Sauer, Dorin Comaniciu, Helene C. Houle, Ingmar Voigt
  • Patent number: 11275976
    Abstract: Medical images may be classified by receiving a first medical image. The medical image may be applied to a machine learned classifier. The machine learned classifier may be trained on second medical images. A label of the medical image and a measure of uncertainty may be generated. The measure of uncertainty may be compared to a threshold. The first medical image and the label may be output when the measure of uncertainty is within the threshold.
    Type: Grant
    Filed: September 5, 2019
    Date of Patent: March 15, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Florin-Cristian Ghesu, Eli Gibson, Bogdan Georgescu, Sasa Grbic, Dorin Comaniciu
  • Patent number: 11244453
    Abstract: Systems and method are described for determining a malignancy of a nodule. A medical image of a nodule of a patient is received. A patch surrounding the nodule is identified in the medical image. A malignancy of the nodule in the patch is predicted using a trained deep image-to-image network.
    Type: Grant
    Filed: November 2, 2018
    Date of Patent: February 8, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Sasa Grbic, Dorin Comaniciu, Bogdan Georgescu, Siqi Liu, Razvan Ionasec