Patents by Inventor Daniel Toth
Daniel Toth 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: 20230326581Abstract: A method for image analysis includes receiving compressed, encrypted image data from an imaging session at a healthcare location, unencrypting the image data, and identifying a characteristic of interest in the compressed image data. The method further includes annotating the compressed image data reflecting the identified characteristic of interest to generate computer annotations and streaming the computer annotations to the healthcare location as the imaging session is in progress.Type: ApplicationFiled: April 10, 2023Publication date: October 12, 2023Inventors: Peter Mountney, Daniel Toth, Vincent Riviere, Sanjith Hebbar
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Patent number: 11361423Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.Type: GrantFiled: June 17, 2020Date of Patent: June 14, 2022Assignee: RecognAIse Technologies Inc.Inventors: Janos Csaba Toth, David Stefan Hauser, Attila Daniel Toth, Melinda Meszaros
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Patent number: 10997717Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.Type: GrantFiled: January 31, 2019Date of Patent: May 4, 2021Assignee: Siemens Healthcare GmbHInventors: Pascal Ceccaldi, Peter Mountney, Daniel Toth, Serkan Cimen
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Patent number: 10929989Abstract: The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.Type: GrantFiled: October 29, 2018Date of Patent: February 23, 2021Assignee: Siemens Healthcare GmbHInventors: Tanja Kurzendorfer, Rui Liao, Tommaso Mansi, Shun Miao, Peter Mountney, Daniel Toth
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Publication number: 20200394784Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.Type: ApplicationFiled: June 17, 2020Publication date: December 17, 2020Inventors: Janos Csaba Toth, David Stefan Hauser, Attila Daniel Toth, Melinda Meszaros
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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: 10813611Abstract: A method of extracting mechanical activation of the left ventricle from a sequence of contrasted X-ray fluoroscopy images is provided. The method includes: processing the image sequence to perform segmentation of the coronary veins; annotating branches of the segmented coronary veins; tracking the positions of the annotations throughout the fluoroscopy image sequence; computing the principle components of the motion of the annotations from the tracked positions; projecting the motion of the annotations to the axis corresponding to a first principle component; and analyzing the resulting motion curves to identify a latest activating region of the left ventricle.Type: GrantFiled: January 28, 2019Date of Patent: October 27, 2020Inventors: Daniel Toth, Peter Mountney, Tanja Kurzendorfer, Christopher A. Rinaldi, Kawal Rhode
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Publication number: 20200250812Abstract: In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.Type: ApplicationFiled: January 31, 2019Publication date: August 6, 2020Applicant: Siemens Healthcare LimitedInventors: Pascal Ceccaldi, Peter Mountney, Daniel Toth, Serkan Cimen
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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: 20190231289Abstract: A method of extracting mechanical activation of the left ventricle from a sequence of contrasted X-ray fluoroscopy images is provided. The method includes: processing the image sequence to perform segmentation of the coronary veins; annotating branches of the segmented coronary veins; tracking the positions of the annotations throughout the fluoroscopy image sequence; computing the principle components of the motion of the annotations from the tracked positions; projecting the motion of the annotations to the axis corresponding to a first principle component; and analyzing the resulting motion curves to identify a latest activating region of the left ventricle.Type: ApplicationFiled: January 28, 2019Publication date: August 1, 2019Inventors: Daniel Toth, Peter Mountney, Tanja Kurzendorfer, Christopher A. Rinaldi, Kawal Rhode
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Patent number: 10332253Abstract: A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.Type: GrantFiled: June 27, 2017Date of Patent: June 25, 2019Inventors: Jonathan Behar, Alexander Brost, Peter Mountney, Maria Panayiotou, Kawal Rhode, Aldo Rinaldi, Daniel Toth
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Publication number: 20190130587Abstract: The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.Type: ApplicationFiled: October 29, 2018Publication date: May 2, 2019Inventors: Tanja Kurzendorfer, Rui Liao, Tommaso Mansi, Shun Miao, Peter Mountney, Daniel Toth
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Patent number: 9990716Abstract: In a method for visualization of scar tissue in medical imaging data of a heart, medical imaging data representing a heart myocardium and scar tissue within the heart myocardium are obtained and provided to a computer. The computer deviates the thickness of the myocardium into a number of layers and calculates the presence and distribution of scar tissue within each of the layers. The scar tissue is shown in a visualization of the myocardium; and a user is provided with controls to allow the user to select which of the layers of scar tissue is visualised.Type: GrantFiled: January 30, 2017Date of Patent: June 5, 2018Assignee: Siemens Healthcare GmbHInventors: Peter Mountney, Sabrina Reiml, Daniel Toth, Alexander Brost, Maria Panayiotou, Kawal Rhode
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Publication number: 20170372474Abstract: A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.Type: ApplicationFiled: June 27, 2017Publication date: December 28, 2017Inventors: Jonathan Behar, Alexander Brost, Peter Mountney, Maria Panayiotou, Kawal Rhode, Aldo Rinaldi, Daniel Toth
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Publication number: 20170221205Abstract: In a method for visualization of scar tissue in medical imaging data of a heart, medical imaging data representing a heart myocardium and scar tissue within the heart myocardium are obtained and provided to a computer. The computer deviates the thickness of the myocardium into a number of layers and calculates the presence and distribution of scar tissue within each of the layers. The scar tissue is shown in a visualization of the myocardium; and a user is provided with controls to allow the user to select which of the layers of scar tissue is visualised.Type: ApplicationFiled: January 30, 2017Publication date: August 3, 2017Applicant: Siemens Healthcare GmbHInventors: Peter Mountney, Sabrina Reiml, Daniel Toth, Alexander Brost, Maria Panayiotou, Kawal Rhode