Patents by Inventor Max Schoebinger
Max Schoebinger 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: 20190057541Abstract: A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.Type: ApplicationFiled: August 17, 2018Publication date: February 21, 2019Applicant: Siemens Healthcare GmbHInventors: Xinyun Li, Rainer Kaergel, Michael Suehling, Chris Schwemmer, Max Schoebinger
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Publication number: 20190038249Abstract: 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.Type: ApplicationFiled: September 28, 2018Publication date: February 7, 2019Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 10111636Abstract: 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.Type: GrantFiled: February 6, 2018Date of Patent: October 30, 2018Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 10052031Abstract: A method for determining functional severity of a stenosis includes: (a) generating a simulated perfusion map from a calculated blood flow; (b) comparing the simulated perfusion map to a measured perfusion map to identify a degree of mismatch therebetween, the measured perfusion map representing perfusion in a patient; (c) modifying a parameter in a model used in calculating the blood flow when the degree of mismatch meets or exceeds a predefined threshold; (d) computing a hemodynamic quantity from the simulated perfusion map when the degree of mismatch is less than the predefined threshold, the hemodynamic quantity being indicative of the functional severity of the stenosis; and (e) displaying the hemodynamic quantity. Systems for determining functional severity of a stenosis are described.Type: GrantFiled: February 14, 2014Date of Patent: August 21, 2018Assignee: Siemens Healthcare GmbHInventors: Puneet Sharma, Ali Kamen, Max Schöbinger, Michael Scheuering, Dorin Comaniciu
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Publication number: 20180153495Abstract: 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.Type: ApplicationFiled: February 6, 2018Publication date: June 7, 2018Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Publication number: 20180116620Abstract: A method and apparatus for deep learning based automatic bone removal in medical images, such as computed tomography angiography (CTA) volumes, is disclosed. Bone structures are segmented in a 3D medical image of a patient by classifying voxels of the 3D medical image as bone or non-bone voxels using a deep neural network trained for bone segmentation. A 3D visualization of non-bone structures in the 3D medical image is generated by removing voxels classified as bone voxels from a 3D visualization of the 3D medical image.Type: ApplicationFiled: October 9, 2017Publication date: May 3, 2018Inventors: Mingqing Chen, Tae Soo Kim, Jan Kretschmer, Sebastian Seifert, Shaohua Kevin Zhou, Max Schöbinger, David Liu, Zhoubing Xu, Sasa Grbic, He Zhang
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Patent number: 9931790Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.Type: GrantFiled: April 16, 2015Date of Patent: April 3, 2018Assignee: Siemens Healthcare GmbHInventors: Sasa Grbic, Razvan Ionasec, Tommaso Mansi, Ingmar Voigt, Dominik Neumann, Julian Krebs, Chris Schwemmer, Max Schoebinger, Helene C. Houle, Dorin Comaniciu, Joel Mancina
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Patent number: 9918690Abstract: 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.Type: GrantFiled: October 7, 2015Date of Patent: March 20, 2018Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 9888968Abstract: A method and system for automated decision support for treatment planning of arterial stenoses is disclosed. A set of stenotic lesions is identified in a patient's coronary arteries from medical image data of the patient. A plurality of treatment options are generated for the set of stenotic lesions, wherein each of the plurality of treatment options corresponds to a stenting configuration in which one or more of the stenotic lesions are stented. For each of the plurality of treatment options, predicted hemodynamic metrics for the set of stenotic lesions resulting from the stenting configuration corresponding to that treatment option are calculated.Type: GrantFiled: July 17, 2015Date of Patent: February 13, 2018Assignee: Siemens Healthcare GmbHInventors: Frank Sauer, Puneet Sharma, Max Schoebinger
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Publication number: 20170245821Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.Type: ApplicationFiled: November 16, 2015Publication date: August 31, 2017Applicant: Siemens Healthcare GmbHInventors: Lucian Mihai ITU, Puneet SHARMA, Saikiran RAPAKA, Tiziano PASSERINI, Max SCHÖBINGER, Chris SCHWEMMER, Dorin COMANICIU, Thomas REDEL
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Publication number: 20160303804Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.Type: ApplicationFiled: April 16, 2015Publication date: October 20, 2016Inventors: Sasa Grbic, Razvan Ionasec, Tommaso Mansi, Ingmar Voigt, Dominik Neumann, Julian Krebs, Chris Schwemmer, Max Schoebinger, Helene C. Houle, Dorin Comaniciu, Joel Mancina
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Publication number: 20160148371Abstract: 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.Type: ApplicationFiled: July 21, 2015Publication date: May 26, 2016Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Publication number: 20160148372Abstract: 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.Type: ApplicationFiled: October 7, 2015Publication date: May 26, 2016Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 9349178Abstract: 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.Type: GrantFiled: July 21, 2015Date of Patent: May 24, 2016Assignee: Siemens AktiengesellschaftInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Publication number: 20160022371Abstract: A method and system for automated decision support for treatment planning of arterial stenoses is disclosed. A set of stenotic lesions is identified in a patient's coronary arteries from medical image data of the patient. A plurality of treatment options are generated for the set of stenotic lesions, wherein each of the plurality of treatment options corresponds to a stenting configuration in which one or more of the stenotic lesions are stented. For each of the plurality of treatment options, predicted hemodynamic metrics for the set of stenotic lesions resulting from the stenting configuration corresponding to that treatment option are calculated.Type: ApplicationFiled: July 17, 2015Publication date: January 28, 2016Inventors: Frank Sauer, Puneet Sharma, Max Schoebinger
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Publication number: 20150104090Abstract: A method and a system are disclosed for interactively creating and/or modifying a hollow organ representation on the basis of medical-technical image data of a hollow organ. An embodiment of the method includes providing the medical-technical image data together with a hollow organ course line representing the course of the hollow organ; providing a plurality of contour representations of a contour of the hollow organ representation along the hollow organ course line; receiving a command input for the input and/or modification of a selected contour representation and/or of the hollow organ course line; locally modifying (H) the contour of the hollow organ representation on the basis of the command input, taking into consideration a number of contour representations adjacent to the selected contour representation on at least one side along the hollow organ course line, using an automatic interpolating sweep algorithm.Type: ApplicationFiled: September 25, 2014Publication date: April 16, 2015Inventors: Christian HOPFGARTNER, Jan KRETSCHMER, Max SCHÖBINGER
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Publication number: 20140249399Abstract: A method for determining functional severity of a stenosis includes: (a) generating a simulated perfusion map from a calculated blood flow; (b) comparing the simulated perfusion map to a measured perfusion map to identify a degree of mismatch therebetween, the measured perfusion map representing perfusion in a patient; (c) modifying a parameter in a model used in calculating the blood flow when the degree of mismatch meets or exceeds a predefined threshold; (d) computing a hemodynamic quantity from the simulated perfusion map when the degree of mismatch is less than the predefined threshold, the hemodynamic quantity being indicative of the functional severity of the stenosis; and (e) displaying the hemodynamic quantity. Systems for determining functional severity of a stenosis are described.Type: ApplicationFiled: February 14, 2014Publication date: September 4, 2014Applicant: Siemens AktiengesellschaftInventors: Puneet Sharma, Ali Kamen, Max Schöbinger, Michael Scheuering, Dorin Comaniciu