-
公开(公告)号：US12004860B2
公开(公告)日：2024-06-11
申请号：US17305391
申请日：2021-07-07
Applicant: SIEMENS HEALTHINEERS AG
Inventor： Paul Klein , Ingo Schmuecking , Costin Florian Ciusdel , Lucian Mihai Itu , Tiziano Passerini , Puneet Sharma
CPC classification number: A61B5/308 , A61B5/026 , A61B5/7267
Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.
-
公开(公告)号：US11931195B2
公开(公告)日：2024-03-19
申请号：US17602820
申请日：2019-07-22
Applicant: Siemens Healthineers AG
Inventor： Lucian Mihai Itu , Diana Ioana Stoian , Tiziano Passerini , Puneet Sharma
CPC classification number: A61B6/504 , A61B6/487 , G06T7/0012 , G16H50/20 , G06T2207/10081 , G06T2207/10121 , G06T2207/30101
Abstract: Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.
-
公开(公告)号：US12198813B2
公开(公告)日：2025-01-14
申请号：US17657208
申请日：2022-03-30
Applicant: Siemens Healthineers AG
Inventor： Viorel Mihalef , Tiziano Passerini , Puneet Sharma
Abstract: Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.
-
公开(公告)号：US20240346665A1
公开(公告)日：2024-10-17
申请号：US18300420
申请日：2023-04-14
Applicant: Siemens Healthineers AG
Inventor： Huseyin Tek , Tiziano Passerini , Ingo Schmuecking
CPC classification number: G06T7/248 , G06T7/0016 , G06T7/73 , G06T2207/10016 , G06T2207/20081 , G06T2207/30048
Abstract: A system includes propagation logic configured to obtain one or more contours for one or more directed viewframes within viewframe data. The one or more contours each having a set of tracking points. The viewframe data further includes intermediate viewframes among the one or more directed viewframes. The propagation logic is configured propagate the one or more contours across the intermediate viewframes via iterative viewframe-to-viewframe propagation. The iterative viewframe-to-viewframe propagation include optical flow analysis to determine candidate locations for tracking points followed by one or more validations using motion priors and/or resolved feature tracking.
-
公开(公告)号：US12175668B2
公开(公告)日：2024-12-24
申请号：US17659208
申请日：2022-04-14
Applicant: SIEMENS HEALTHINEERS AG
Inventor： Ingo Schmuecking , Puneet Sharma , Desiree Komuves , Tiziano Passerini , Paul Klein
Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.
-
Patent Agency Ranking
公开(公告)号：US12109061B2
公开(公告)日：2024-10-08
申请号：US17195694
申请日：2021-03-09
Applicant: SIEMENS HEALTHINEERS AG
Inventor： Lucian Mihai Itu , Tiziano Passerini , Saikiran Rapaka , Puneet Sharma , Chris Schwemmer , Max Schoebinger , Thomas Redel , Dorin Comaniciu
IPC: G06T7/11 , A61B5/00 , A61B5/026 , A61B6/00 , A61B6/03 , A61B6/50 , A61B8/06 , A61B8/08 , G06F18/21 , G06F18/22 , G06F18/2413 , G06T7/00 , G06V10/42 , G06V10/776 , G16H20/00 , G16H30/40 , G16H50/20 , G16H50/50 , A61B5/02 , A61B6/46 , A61B8/00 , G16H30/20
CPC classification number: A61B6/5217 , A61B5/026 , A61B5/7267 , A61B6/032 , A61B6/504 , A61B6/507 , A61B8/06 , A61B8/065 , A61B8/5223 , G06F18/217 , G06F18/22 , G06F18/2413 , G06T7/0012 , G06T7/11 , G06V10/42 , G06V10/776 , G16H20/00 , G16H30/40 , G16H50/20 , G16H50/50 , A61B5/02007 , A61B5/02028 , A61B5/0263 , A61B5/743 , A61B6/469 , A61B8/469 , A61B2576/00 , G06T2200/04 , G06T2207/10072 , G06T2207/10076 , G06T2207/20081 , G06T2207/30101 , G06T2207/30104 , G16H30/20
Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.
-
公开(公告)号：US20240331860A9
公开(公告)日：2024-10-03
申请号：US17655001
申请日：2022-03-16
Applicant: SIEMENS HEALTHINEERS AG
Inventor： Poikavila Ullaskrishnan , Tiziano Passerini , Puneet Sharma , Paul Klein , Teodora-Vanessa Liliac , Larisa Micu
IPC: G16H50/20
CPC classification number: G16H50/20
Abstract: A medical knowledge base in a digital, clinical system is upgraded. A storage with a knowledge base, being a SNOMED knowledge base, is provided in a web ontology format. Procedural data, representing clinical procedures for evaluation of a patient's health state, is received. The received procedural data is mapped in a set of SNOMED expressions. The SNOMED expressions are converted into statements in the web ontology format. The SNOMED knowledge base is upgraded with the received procedural data by adding the statements in the SNOMED knowledge base for providing a processable file with an upgraded version of the SNOMED knowledge base.
-
公开(公告)号：US20240423575A1
公开(公告)日：2024-12-26
申请号：US18825275
申请日：2024-09-05
Applicant: Siemens Healthineers AG
Inventor： Lucian Mihai Itu , Tiziano Passerini , Saikiran Rapaka , Puneet Sharma , Chris Schwemmer , Max Schoebinger , Thomas Redel , Dorin Comaniciu
IPC: A61B6/00 , A61B5/00 , A61B5/02 , A61B5/026 , A61B6/03 , A61B6/46 , A61B6/50 , A61B8/00 , A61B8/06 , A61B8/08 , G06F18/21 , G06F18/22 , G06F18/2413 , G06T7/00 , G06T7/11 , G06V10/42 , G06V10/776 , G16H20/00 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50
Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.
-
9.发明公开公开(公告)号：US20240161285A1
公开(公告)日：2024-05-16
申请号：US18465447
申请日：2023-09-12
Applicant: Siemens Healthineers AG
Inventor： Dominik Neumann , Alexandru Turcea , Lucian Mihai Itu , Tiziano Passerini , Mehmet Akif Gulsun , Martin Berger
CPC classification number: G06T7/0012 , G06T3/40 , G06T7/60 , G06T2207/10116 , G06T2207/30048 , G06T2207/30101 , G06T2207/30168
Abstract: Various aspects of the disclosure generally pertain to determining estimates of hemodynamic properties based on angiographic x-ray examinations of a coronary system. Various aspects of the disclosure specifically pertain to determining such estimates based on single frame metrics operating on two-dimensional images. For example, the fractional flow reserve (FFR) can be computed.
-
公开(公告)号：US12062168B2
公开(公告)日：2024-08-13
申请号：US17305631
申请日：2021-07-12
Applicant: SIEMENS HEALTHINEERS AG
Inventor： Felix Meister , Tiziano Passerini , Tommaso Mansi , Eric Lluch Alvarez , Chloé Audigier , Viorel Mihalef
IPC: G06T7/00 , A61B6/00 , G06F18/22 , G06F18/231 , G06F18/243 , G06T7/11 , A61B6/50
CPC classification number: G06T7/0012 , A61B6/5217 , G06F18/22 , G06F18/231 , G06F18/24323 , G06T7/11 , A61B6/50 , G06T2207/20076 , G06T2207/30061 , G06V2201/03
Abstract: Systems and methods for estimating local conductivities from anatomical information derived from MR images, ECG, and sparse contact maps are provided. ECG features and sparse measurements are mapped to an anatomical model represented as a graph. Graph convolutional layers and a multilayer perceptron are applied to extract local and global features respectively. The local and global features are combined and further processed by a series of fully connected layers to regress a set of vertex conductivities.
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.015 seconds)
