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公开(公告)号：US20230368383A1
公开(公告)日：2023-11-16
申请号：US18351548
申请日：2023-07-13
Applicant: Siemens Healthcare GmbH
Inventor： 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
CPC classification number: G06T7/0012 , G06T7/30 , A61B5/7267 , G06T2207/20081
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.
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公开(公告)号：US20200258227A1
公开(公告)日：2020-08-13
申请号：US16861353
申请日：2020-04-29
Applicant: Siemens Healthcare GmbH
Inventor： 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
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.
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公开(公告)号：US20200020098A1
公开(公告)日：2020-01-16
申请号：US16483887
申请日：2017-11-03
Applicant: Siemens Healthcare GmbH
Inventor： Benjamin L. Odry , Dorin Comaniciu , Bogdan Georgescu , Mariappan S. Nadar
Abstract: A method for processing medical image data comprises: inputting medical image data to a variational autoencoder configured to reduce a dimensionality of the medical image data to a latent space having one or more latent variables with latent variable values, such that the latent variable values corresponding to an image with no tissue of a target tissue type fit within one or more clusters; determining a probability that the latent variable values corresponding to the medical image data fit within the one or more clusters based on the latent variable values; and determining that a tissue of the target tissue type is present in response to a determination that the medical image data have less than a threshold probability of fitting within any of the one or more clusters based on the latent variable values.
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公开(公告)号：US10467495B2
公开(公告)日：2019-11-05
申请号：US15564036
申请日：2015-05-11
Applicant: Siemens Healthcare GmbH
Inventor： David Liu , Bogdan Georgescu , Yefeng Zheng , Hien Nguyen , Shaohua Kevin Zhou , Vivek Kumar Singh , Dorin Comaniciu
Abstract: A method and system for anatomical landmark detection in medical images using deep neural networks is disclosed. For each of a plurality of image patches centered at a respective one of a plurality of voxels in the medical image, a subset of voxels within the image patch is input to a trained deep neural network based on a predetermined sampling pattern. A location of a target landmark in the medical image is detected using the trained deep neural network based on the subset of voxels input to the trained deep neural network from each of the plurality of image patches.
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公开(公告)号：US20190205606A1
公开(公告)日：2019-07-04
申请号：US16094900
申请日：2017-07-19
Applicant: Siemens Healthcare GmbH
Inventor： Shaohua Kevin Zhou , Mingqing Chen , Hui Ding , Bogdan Georgescu , Mehmet Akif Gulsun , Tae Soo Kim , Atilla Peter Kiraly , Xiaoguang Lu , Jin-hyeong Park , Puneet Sharma , Shanhui Sun , Daguang Xu , Zhoubing Xu , Yefeng Zheng
CPC classification number: G06K9/0014 , G06K9/4628 , G06K9/6209 , G06N3/0445 , G06N3/0454 , G06N3/084 , G06T7/11 , G16H30/40
Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.
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Patent Agency Ranking
公开(公告)号：US10299862B2
公开(公告)日：2019-05-28
申请号：US15016442
申请日：2016-02-05
Applicant: Siemens Healthcare GmbH
Inventor： Bogdan Georgescu , Lucian Mihai Itu , Ali Kamen , Tommaso Mansi , Viorel Mihalef , Tiziano Passerini , Rapaka Saikiran , Puneet Sharma
Abstract: A medical system is provided for three-dimensional hemodynamic quantification. Comprehensive three-dimensional (3D) plus time (3D+t) assessment of flow patterns inside the heart are provided by a combination of lumped-parameter modeling and computational flow dynamic modeling. Using medical scanning, the lumped parameter model is personalized to a given patient. The personalized lumped-parameter model provides pressure curves (i.e., pressure as a function of time) for one or more locations. Using geometry of the patients heart segmented from the medical scanning and the pressure curves as boundary conditions, the computational flow dynamics model calculates the absolute pressure for any location (e.g., for a three-dimensional field of locations) in the patient heart at any one or more phases of the cardiac cycle. More accurate absolute pressure may be provided without invasive measurement.
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7.发明授权公开(公告)号：US10296707B2
公开(公告)日：2019-05-21
申请号：US14683245
申请日：2015-04-10
Applicant: Siemens Healthcare GmbH
Inventor： Tiziano Passerini , Tommaso Mansi , Ali Kamen , Bogdan Georgescu , Dorin Comaniciu
Abstract: A method and system for image-based patient-specific guidance of cardiac arrhythmia therapies is disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific cardiac electrophysiology model is generated based on the patient-specific anatomical heart model and electrophysiology measurements of the patient. One or more virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. One or more pacing targets or ablation targets based on the one or more virtual electrophysiological interventions are displayed.
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公开(公告)号：US10068669B2
公开(公告)日：2018-09-04
申请号：US14655083
申请日：2014-01-20
Applicant: Siemens Healthcare GmbH
Inventor： Tommaso Mansi , Oliver Zettinig , Bogdan Georgescu , Ali Kamen , Dorin Comaniciu , Saikiran Rapaka
IPC: G06F19/26 , G16H50/50 , G06T7/00 , G06T11/60 , G06T7/20 , G06T11/20 , G06T7/12 , G06T7/149 , G06F19/00
Abstract: A method and system for simulating cardiac function of a patient. A patient-specific anatomical model of at least a portion of the patient's heart is generated from medical image data. Cardiac electrophysiology potentials are calculated over a computational domain defined by the patient-specific anatomical model for each of a plurality of time steps using a patient-specific cardiac electrophysiology model. The electrophysiology potentials acting on a plurality of nodes of the computational domain are calculated in parallel for each time step. Biomechanical forces are calculated over the computational domain for each of the plurality of time steps using a cardiac biomechanical model coupled to the cardiac electrophysiology model. The biomechanical forces acting on a plurality of nodes of the computational domain are estimated in parallel for each time step. Blood flow and cardiac movement are computed at each of the plurality of time steps based on the calculated biomechanical forces.
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公开(公告)号：US20180242857A1
公开(公告)日：2018-08-30
申请号：US15958483
申请日：2018-04-20
Applicant: Siemens Healthcare GmbH
Inventor： Puneet Sharma , Ali Kamen , Bogdan Georgescu , Frank Sauer , Dorin Comaniciu , Yefeng Zheng , Hien Nguyen , Vivek Kumar Singh
IPC: A61B5/026 , G16H50/30 , G16H50/20 , A61B5/00 , A61B6/03 , A61B6/00 , A61B8/06 , G06K9/46 , G06K9/62 , G06T7/00 , G06T7/20
CPC classification number: A61B5/026 , A61B5/0261 , A61B5/0263 , A61B5/7264 , A61B5/7267 , A61B5/7282 , A61B6/032 , A61B6/504 , A61B6/507 , A61B6/5217 , A61B6/563 , A61B8/06 , A61B8/0891 , A61B8/12 , A61B8/5223 , A61B8/565 , G06K9/46 , G06K9/6256 , G06T7/0012 , G06T7/0016 , G06T7/20 , G06T2207/10081 , G06T2207/10088 , G06T2207/10101 , G06T2207/10132 , G06T2207/20081 , G06T2207/30048 , G06T2207/30104 , G06T2211/404 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.
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公开(公告)号：US09984772B2
公开(公告)日：2018-05-29
申请号：US15455591
申请日：2017-03-10
Applicant: Siemens Healthcare GmbH
Inventor： Wen Liu , Ashutosh Modi , Bogdan Georgescu , Francisco Pereira
CPC classification number: G16H50/20 , G06F17/271 , G06F17/2785 , G06F17/279 , G06F17/3043 , G06F17/30654 , G06F19/00 , G06F19/321 , G06N3/0445 , G06N3/08
Abstract: A computer-implemented method for predicting answers to questions concerning medical image analytics reports includes splitting a medical image analytics report into a plurality of sentences and generating a plurality of sentence embedding vectors by applying a natural language processing framework to the plurality of sentences. A question related to subject matter included in the medical image analytics report is received and a question embedding vector is generated by applying the natural language processing framework to the question. A subset of the sentence embedding vectors most similar to the question embedding vector is identified by applying a similarity matching process to the sentence embedding vectors and the question embedding vector. A trained recurrent neural network (RNN) is used to determine a predicted answer to the question based on the subset of the sentence embedding vectors.
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