Patents by Inventor Julian Krebs
Julian Krebs 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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Publication number: 20230368383Abstract: 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: ApplicationFiled: July 13, 2023Publication date: November 16, 2023Inventors: 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
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Patent number: 11741605Abstract: 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: GrantFiled: December 12, 2022Date of Patent: August 29, 2023Assignee: Siemens Healthcare GmbHInventors: 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
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Patent number: 11631500Abstract: Systems and methods for predicting a patient specific risk of cardiac events for cardiac arrhythmia are provided. A medical image sequence of a heart of a patient is received. Cardiac function features are extracted from the medical image sequence. Additional features are extracted from patient data of the patient. A patient specific risk of a cardiac event is predicted based on the extracted cardiac function features and the extracted additional features.Type: GrantFiled: April 24, 2020Date of Patent: April 18, 2023Assignee: Siemens Healthcare GmbHInventors: Julian Krebs, Tommaso Mansi, Bin Lou
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Publication number: 20230114934Abstract: 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: ApplicationFiled: December 12, 2022Publication date: April 13, 2023Inventors: 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
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Patent number: 11557036Abstract: 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: GrantFiled: April 29, 2020Date of Patent: January 17, 2023Assignee: Siemens Healthcare GmbHInventors: 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
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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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Patent number: 11449759Abstract: 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: GrantFiled: December 27, 2018Date of Patent: September 20, 2022Assignees: Siemens Heathcare GmbH, Institut National de Recherche en Informatique et en AutomatiqueInventors: Julian Krebs, Herve Delingette, Nicholas Ayache, Tommaso Mansi, Shun Miao
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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: 11403761Abstract: 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: GrantFiled: March 30, 2020Date of Patent: August 2, 2022Assignees: Siemens Healthcare GmbH, Institut National de Recherche en Informatique et en AutomatiqueInventors: Julian Krebs, Tommaso Mansi, Herve Delingette, Nicholas Ayache
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Patent number: 11350888Abstract: 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: GrantFiled: April 10, 2020Date of Patent: June 7, 2022Assignees: Siemens Healthcare GmbH, The Johns Hopkins UniversityInventors: Julian Krebs, Hiroshi Ashikaga, Tommaso Mansi, Bin Lou, Katherine Chih-ching Wu, Henry Halperin
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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: 20210059612Abstract: 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: ApplicationFiled: April 10, 2020Publication date: March 4, 2021Inventors: Julian Krebs, Hiroshi Ashikaga, Tommaso Mansi, Bin Lou, Katherine Chih-ching Wu, Henry Halperin
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Publication number: 20210057104Abstract: Systems and methods for predicting a patient specific risk of cardiac events for cardiac arrhythmia are provided. A medical image sequence of a heart of a patient is received. Cardiac function features are extracted from the medical image sequence. Additional features are extracted from patient data of the patient. A patient specific risk of a cardiac event is predicted based on the extracted cardiac function features and the extracted additional features.Type: ApplicationFiled: April 24, 2020Publication date: February 25, 2021Inventors: Julian Krebs, Tommaso Mansi, Bin Lou
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Publication number: 20200311940Abstract: 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: ApplicationFiled: March 30, 2020Publication date: October 1, 2020Inventors: Julian Krebs, Tommaso Mansi, Herve Delingette, Nicholas Ayache
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Publication number: 20200258227Abstract: 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: ApplicationFiled: April 29, 2020Publication date: August 13, 2020Inventors: 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
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Patent number: 10664979Abstract: A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.Type: GrantFiled: September 14, 2018Date of Patent: May 26, 2020Assignee: Siemens Healthcare GmbHInventors: Julian Krebs, Tommaso Mansi
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Patent number: 10607342Abstract: Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.Type: GrantFiled: June 16, 2017Date of Patent: March 31, 2020Assignee: Siemenes Healthcare GmbHInventors: Li Zhang, Shanhui Sun, Shaohua Kevin Zhou, Daguang Xu, Zhoubing Xu, Tommaso Mansi, Ying Chi, Yefeng Zheng, Pavlo Dyban, Nora Hünemohr, Julian Krebs, David Liu
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Publication number: 20200090345Abstract: A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.Type: ApplicationFiled: September 14, 2018Publication date: March 19, 2020Inventors: Julian Krebs, Tommaso Mansi
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Publication number: 20190205766Abstract: 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: ApplicationFiled: December 27, 2018Publication date: July 4, 2019Inventors: Julian Krebs, Herve Delingette, Nicholas Ayache, Tommaso Mansi, Shun Miao
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Publication number: 20180096478Abstract: Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.Type: ApplicationFiled: June 16, 2017Publication date: April 5, 2018Inventors: Li Zhang, Shanhui Sun, Shaohua Kevin Zhou, Daguang Xu, Zhoubing Xu, Tommaso Mansi, Ying Chi, Yefeng Zheng, Pavlo Dyban, Nora Hünemohr, Julian Krebs, David Liu