Patents by Inventor Sébastien Piat
Sébastien Piat 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).
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Patent number: 11908047Abstract: 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: GrantFiled: March 11, 2021Date of Patent: February 20, 2024Assignee: Siemens Healthineers AGInventors: Boris Mailhe, Florin-Cristian Ghesu, Siqi Liu, Sasa Grbic, Sebastian Vogt, Dorin Comaniciu, Awais Mansoor, Sebastien Piat, Steffen Kappler, Ludwig Ritschl
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Patent number: 11810291Abstract: 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: GrantFiled: May 1, 2020Date of Patent: November 7, 2023Assignee: Siemens Healthcare GmbHInventors: Siqi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastien Piat, Brian Teixeira, Thomas Re, Dorin Comaniciu
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Publication number: 20230157761Abstract: 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: ApplicationFiled: November 24, 2021Publication date: May 25, 2023Inventors: 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
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Publication number: 20220375073Abstract: DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.Type: ApplicationFiled: May 5, 2022Publication date: November 24, 2022Inventors: Ingmar Voigt, Marcel Dominik Nickel, Tommaso Mansi, Sebastien Piat
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Patent number: 11475535Abstract: CT and PET are registered, providing a spatial alignment to be used in attenuation correction for PET reconstruction. A model for machine learning is defined to generate a deformation field. The model is trained with loss based, in part, on the attenuation corrected PET data rather than or in addition to loss based on the uncorrected PET or the generated deformation field. Due to the nature of the mapping from CT to attenuation, a separate, pre-trained network is used to form the attenuation corrected PET data in training the model.Type: GrantFiled: May 6, 2020Date of Patent: October 18, 2022Assignee: Siemens Medical Solutions USA, Inc.Inventors: Sebastien Piat, Julian Krebs
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Publication number: 20220292742Abstract: 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: ApplicationFiled: March 11, 2021Publication date: September 15, 2022Inventors: Boris Mailhe, Florin-Cristian Ghesu, Siqi Liu, Sasa Grbic, Sebastian Vogt, Dorin Comaniciu, Awais Mansoor, Sebastien Piat, Steffen Kappler, Ludwig Ritschl
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Patent number: 11430121Abstract: 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: GrantFiled: April 1, 2020Date of Patent: August 30, 2022Assignee: Siemens Healthcare GmbHInventors: 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
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Publication number: 20220270256Abstract: Systems and methods for medical image registration are provided. A first input medical image and a second input medical image of one or more anatomical objects arc received. For each respective anatomical object of the one or more anatomical objects, a region of interest comprising the respective anatomical object is detected in one of the first input medical image or the second input medical image, the region of interest is extracted from the first input medical image and from the second input medical image, and a motion distribution of the respective anatomical object is determined from one of the region of interest extracted from the first input medical image or the region of interest extracted from the second input medical image using a motion model specific to the respective anatomical object. The first input medical image and the second input medical image are registered based on the motion distribution of each respective anatomical object of the one or more anatomical objects to generate a fused image.Type: ApplicationFiled: December 13, 2019Publication date: August 25, 2022Inventors: Julian Krebs, Sebastien Piat
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Patent number: 11354813Abstract: 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: GrantFiled: September 24, 2020Date of Patent: June 7, 2022Assignee: Siemens Healthcare GmbHInventors: Sébastien Piat, Shun Miao, Rui Liao, Tommaso Mansi, Jiannan Zheng
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Publication number: 20210327054Abstract: 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: ApplicationFiled: May 1, 2020Publication date: October 21, 2021Inventors: Siqi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastien Piat, Brian Teixeira, Thomas Re, Dorin Comaniciu
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Publication number: 20210304408Abstract: 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: ApplicationFiled: April 1, 2020Publication date: September 30, 2021Inventors: 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
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Patent number: 10977558Abstract: In a method and apparatus for training a computer system for use in classification of an image by processing image data representing the image, image data are compressed and then loaded into a programmable quantum annealing device that includes a Restricted Boltzmann Machine. The Restricted Boltzmann Machine is trained to act as a classifier of image data, thereby providing a trained Restricted Boltzmann Machine; and, the trained Restricted Boltzmann Machine is used to initialize a neural network for image classification thereby providing a trained computer system for use in classification of an image.Type: GrantFiled: February 1, 2019Date of Patent: April 13, 2021Assignee: Siemens Healthcare GmbHInventors: Mark Herbster, Peter Mountney, Sebastien Piat, Simone Severini
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Publication number: 20210090212Abstract: CT and PET are registered, providing a spatial alignment to be used in attenuation correction for PET reconstruction. A model for machine learning is defined to generate a deformation field. The model is trained with loss based, in part, on the attenuation corrected PET data rather than or in addition to loss based on the uncorrected PET or the generated deformation field. Due to the nature of the mapping from CT to attenuation, a separate, pre-trained network is used to form the attenuation corrected PET data in training the model.Type: ApplicationFiled: May 6, 2020Publication date: March 25, 2021Inventors: Sebastien Piat, Julian Krebs
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Publication number: 20210012514Abstract: 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: ApplicationFiled: September 24, 2020Publication date: January 14, 2021Inventors: Sébastien Piat, Shun Miao, Rui Liao, Tommaso Mansi, Jiannan Zheng
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Patent number: 10832392Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.Type: GrantFiled: December 19, 2018Date of Patent: November 10, 2020Assignee: Siemens Healthcare GmbHInventors: Pascal Ceccaldi, Tanja Kurzendorfer, Tommaso Mansi, Peter Mountney, Sebastien Piat, Daniel Toth
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Patent number: 10818019Abstract: 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: GrantFiled: August 14, 2018Date of Patent: October 27, 2020Assignee: Siemens Healthcare GmbHInventors: Sebastien Piat, Shun Miao, Rui Liao, Tommaso Mansi, Jiannan Zheng
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Publication number: 20200202507Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.Type: ApplicationFiled: December 19, 2018Publication date: June 25, 2020Applicant: Siemens Healthcare GmbHInventors: Pascal Ceccaldi, Tanja Kurzendorfer, Tommaso Mansi, Peter Mountney, Sebastien Piat, Daniel Toth
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Publication number: 20200005154Abstract: In a method and apparatus for training a computer system for use in classification of an image by processing image data representing the image, image data are compressed and then loaded into a programmable quantum annealing device that includes a Restricted Boltzmann Machine. The Restricted Boltzmann Machine is trained to act as a classifier of image data, thereby providing a trained Restricted Boltzmann Machine; and, the trained Restricted Boltzmann Machine is used to initialize a neural network for image classification thereby providing a trained computer system for use in classification of an image.Type: ApplicationFiled: February 1, 2019Publication date: January 2, 2020Applicant: Siemens Healthcare LimitedInventors: Mark Herbster, Peter Mountney, Sebastien Piat, Simone Severini
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Publication number: 20190050999Abstract: 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: ApplicationFiled: August 14, 2018Publication date: February 14, 2019Inventors: Sébastien Piat, Shun Miao, Rui Liao, Tommaso Mansi, Jiannan Zheng