Patents by Inventor Tommaso Mansi

Tommaso Mansi 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: 20220296306
    Abstract: An ultrasound imager provides for LAA closure guidance. Using ultrasound imaging allows for modeling over time (e.g., throughout a heart cycle). An anatomy model of the LAA over time is used to create a biomechanical model personalized to the patient. The personalized models and a model of one or more closure devices are used to select a closure device for the patient appropriate for the entire heart cycle and to guide placement of the selected closure device during an implantation.
    Type: Application
    Filed: June 8, 2022
    Publication date: September 22, 2022
    Inventors: Estelle Camus, Tommaso Mansi, Ingmar Voigt
  • Patent number: 11445994
    Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.
    Type: Grant
    Filed: January 24, 2018
    Date of Patent: September 20, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Tommaso Mansi, Tiziano Passerini, Puneet Sharma, Terrence Chen, Ahmet Tuysuzoglu, Shun Miao, Alexander Brost
  • Patent number: 11449759
    Abstract: For registration of medical images with deep learning, a neural network is designed to include a diffeomorphic layer in the architecture. The network may be trained using supervised or unsupervised approaches. By enforcing the diffeomorphic characteristic in the architecture of the network, the training of the network and application of the learned network may provide for more regularized and realistic registration.
    Type: Grant
    Filed: December 27, 2018
    Date of Patent: September 20, 2022
    Assignees: Siemens Heathcare GmbH, Institut National de Recherche en Informatique et en Automatique
    Inventors: Julian Krebs, Herve Delingette, Nicholas Ayache, Tommaso Mansi, Shun Miao
  • Patent number: 11432875
    Abstract: An ultrasound imager provides for LAA closure guidance. Using ultrasound imaging allows for modeling over time (e.g., throughout a heart cycle). An anatomy model of the LAA over time is used to create a biomechanical model personalized to the patient. The personalized models and a model of one or more closure devices are used to select a closure device for the patient appropriate for the entire heart cycle and to guide placement of the selected closure device during an implantation.
    Type: Grant
    Filed: September 28, 2017
    Date of Patent: September 6, 2022
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Estelle Camus, Tommaso Mansi, Ingmar Voigt
  • Patent number: 11403761
    Abstract: Systems and methods for performing a medical imaging analysis task using a machine learning based motion model are provided. One or more medical images of an anatomical structure are received. One or more feature vectors are determined. The one or more feature vectors are mapped to one or more motion vectors using the machine learning based motion model. One or more deformation fields representing motion of the anatomical structure are determined based on the one or more motion vectors and at least one of the one or more medical images. A medical imaging analysis task is performed using the one or more deformation fields.
    Type: Grant
    Filed: March 30, 2020
    Date of Patent: August 2, 2022
    Assignees: Siemens Healthcare GmbH, Institut National de Recherche en Informatique et en Automatique
    Inventors: Julian Krebs, Tommaso Mansi, Herve Delingette, Nicholas Ayache
  • 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: 11354813
    Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.
    Type: Grant
    Filed: September 24, 2020
    Date of Patent: June 7, 2022
    Assignee: Siemens Healthcare GmbH
    Inventors: Sébastien Piat, Shun Miao, Rui Liao, Tommaso Mansi, Jiannan Zheng
  • Patent number: 11350888
    Abstract: Systems and methods for personalized sudden cardiac death risk prediction that generates fingerprints of imaging features of cardiac structure and function. One or more fingerprints and clinical data may be used to generate a risk score. The output risk score may be used to predict the time of death in order to select high-risk patients for implantable cardioverter-defibrillator treatment.
    Type: Grant
    Filed: April 10, 2020
    Date of Patent: June 7, 2022
    Assignees: Siemens Healthcare GmbH, The Johns Hopkins University
    Inventors: Julian Krebs, Hiroshi Ashikaga, Tommaso Mansi, Bin Lou, Katherine Chih-ching Wu, Henry Halperin
  • 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
  • Publication number: 20220020145
    Abstract: Systems and methods for automatically detecting a disease in medical images are provided. Input medical images are received. A plurality of metrics for a disease is computed for each of the input medical images. The input medical images are clustered into a plurality of clusters based on one or more of the plurality of metrics to classify the input medical images. The plurality of clusters comprise a cluster of one or more of the input medical images associated with the disease and one or more clusters of one or more of the input medical images not associated with the disease. In one embodiment, the disease is COVID-19 (coronavirus disease 2019).
    Type: Application
    Filed: July 12, 2021
    Publication date: January 20, 2022
    Inventors: Felix Meister, Tiziano Passerini, Tommaso Mansi, Eric Lluch Alvarez, Chloé Audigier, Viorel Mihalef
  • Patent number: 11185231
    Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.
    Type: Grant
    Filed: March 25, 2020
    Date of Patent: November 30, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Florin Cristian Ghesu, Yefeng Zheng, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Patent number: 11158069
    Abstract: In order to reduce computation time and provide more accurate solutions for bi-directional, multi-modal image registration, a learning-based unsupervised multi-modal deformable image registration method that does not require any aligned image pairs or anatomical landmarks is provided. A bi-directional registration function is learned based on disentangled shape representation by optimizing a similarity criterion defined on both latent space and image space.
    Type: Grant
    Filed: May 31, 2019
    Date of Patent: October 26, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Bibo Shi, Chen Qin, Rui Liao, Tommaso Mansi, Ali Kamen
  • Patent number: 11151732
    Abstract: Systems and methods for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.
    Type: Grant
    Filed: January 16, 2020
    Date of Patent: October 19, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Bibo Shi, Luis Carlos Garcia-Peraza Herrera, Ankur Kapoor, Mehmet Akif Gulsun, Tiziano Passerini, Tommaso Mansi
  • Patent number: 11132792
    Abstract: Systems and method are described for medical image segmentation. A medical image of a patient in a first domain is received. The medical image comprises one or more anatomical structures. A synthesized image in a second domain is generated from the medical image of the patient in the first domain using a generator of a task driven generative adversarial network. The one or more anatomical structures are segmented from the synthesized image in the second domain using a dense image-to-image network of the task driven generative adversarial network. Results of the segmenting of the one or more anatomical structures from the synthesized image in the second domain represent a segmentation of the one or more anatomical structures in the medical image of the patient in the first domain.
    Type: Grant
    Filed: February 8, 2019
    Date of Patent: September 28, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Yue Zhang, Shun Miao, Rui Liao, Tommaso Mansi, Zengming Shen
  • Publication number: 20210259773
    Abstract: Systems and methods for performing a simulation for an anatomical object of interest are provided. A physiological model of an anatomical object of interest of a patient is generated. Electroanatomical mapping data of the anatomical object of interest is received. The physiological model is updated based on the electroanatomical mapping data of the anatomical object of interest. A simulation for the anatomical object of interest is performed using the updated physiological model. Results of the simulation are output.
    Type: Application
    Filed: January 7, 2021
    Publication date: August 26, 2021
    Inventors: Chloé Audigier, Tiziano Passerini, Eric Lluch Alvarez, Viorel Mihalef, Tommaso Mansi
  • Publication number: 20210264644
    Abstract: A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.
    Type: Application
    Filed: February 12, 2021
    Publication date: August 26, 2021
    Inventors: Yue Zhang, Abdoul Amadou, Ingmar Voigt, Viorel Mihalef, Rui Liao, Tommaso Mansi, Matthias John, Bimba Rao, Helene C. Houle
  • Publication number: 20210248741
    Abstract: The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.
    Type: Application
    Filed: February 5, 2021
    Publication date: August 12, 2021
    Applicant: Siemens Healthcare GmbH
    Inventors: Stephan Kannengiesser, Berthold Kiefer, Tommaso Mansi, Marcel Dominik Nickel, Thomas Pheiffer
  • Publication number: 20210225015
    Abstract: Systems and methods for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.
    Type: Application
    Filed: January 16, 2020
    Publication date: July 22, 2021
    Inventors: Bibo Shi, Luis Carlos Garcia-Peraza Herrera, Ankur Kapoor, Mehmet Akif Gulsun, Tiziano Passerini, Tommaso Mansi
  • Patent number: D935609
    Type: Grant
    Filed: November 19, 2019
    Date of Patent: November 9, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Christian DeBuys, Young-Ho Kim, Ankur Kapoor, Tommaso Mansi