Patents by Inventor Nathan Lay
Nathan Lay 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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Patent number: 10079071Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.Type: GrantFiled: June 28, 2018Date of Patent: September 18, 2018Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
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Patent number: 10037603Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.Type: GrantFiled: May 4, 2015Date of Patent: July 31, 2018Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
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Patent number: 9799135Abstract: The present embodiments relate to cinematic volume renderings and volumetric Monte-Carlo path tracing. The present embodiments include systems and methods for integrating semantic information into cinematic volume renderings. Scan data of a volume is captured by a scanner and transmitted to a server or workstation for rendering. The scan data is received by a server or workstation. The server or workstation extracts semantic information and/or applies semantic processing to the scan data. A cinematic volume rendering is generated from the scan data and the extracted semantic information.Type: GrantFiled: September 1, 2015Date of Patent: October 24, 2017Assignee: Siemens Healthcare GmbHInventors: Shaohua Kevin Zhou, Klaus Engel, David Liu, Daphne Yu, Bernhard Geiger, Nathan Lay
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Patent number: 9633306Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.Type: GrantFiled: May 7, 2015Date of Patent: April 25, 2017Assignee: Siemens Healthcare GmbHInventors: David Liu, Nathan Lay, Shaohua Kevin Zhou, Jan Kretschmer, Hien Nguyen, Vivek Kumar Singh, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu
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Patent number: 9589211Abstract: Systems and methods for segmenting a structure of interest in medical imaging data include generating a binary mask highlighting structures in medical imaging data, the highlighted structures comprising a connected component including a structure of interest. A probability map is computed by classifying voxels in the highlighted structures using a trained classifier. A plurality of detaching operations is performed on the highlighted structures to split the connected component into a plurality of detached connected components. An optimal detaching parameter is determined representing a number of the detaching operations. A detached connected component resulting from performing the number of detaching operations corresponding to the optimal detaching parameter is classified as the structure of interest based on the probability map and the trained classifier.Type: GrantFiled: May 8, 2015Date of Patent: March 7, 2017Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, David Liu, Jan Kretschmer, Shaohua Kevin Zhou
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Publication number: 20170061672Abstract: The present embodiments relate to cinematic volume renderings and volumetric Monte-Carlo path tracing. The present embodiments include systems and methods for integrating semantic information into cinematic volume renderings. Scan data of a volume is captured by a scanner and transmitted to a server or workstation for rendering. The scan data is received by a server or workstation. The server or workstation extracts semantic information and/or applies semantic processing to the scan data. A cinematic volume rendering is generated from the scan data and the extracted semantic information.Type: ApplicationFiled: September 1, 2015Publication date: March 2, 2017Inventors: Shaohua Kevin Zhou, Klaus Engel, David Liu, Daphne Yu, Bernhard Geiger, Nathan Lay
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Patent number: 9576356Abstract: Systems and methods for training a region clustering forest include receiving a set of medical training images for a population of patients. A set of image patches is extracted from each image in the set of medical training images. A plurality of region clustering trees are generated each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest. Each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.Type: GrantFiled: May 8, 2015Date of Patent: February 21, 2017Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, Dong Yang, David Liu, Shaohua Kevin Zhou
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Patent number: 9558568Abstract: A visualization method is provided that allows for the unfolding of a human skeleton from a medical image scan and providing increased efficiency for interacting with the image scan and whole body bone reading from such scans. That is, a full head-to-toe unfolded skeleton view (e.g., a 2D unfolded view) is realized for improved visualization and diagnostic capabilities.Type: GrantFiled: May 7, 2015Date of Patent: January 31, 2017Assignee: Siemens Healthcare GmbHInventors: Jan Kretschmer, Nathan Lay, Shaohua Kevin Zhou
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Publication number: 20160328631Abstract: Systems and methods for segmenting a structure of interest in medical imaging data include generating a binary mask highlighting structures in medical imaging data, the highlighted structures comprising a connected component including a structure of interest. A probability map is computed by classifying voxels in the highlighted structures using a trained classifier. A plurality of detaching operations is performed on the highlighted structures to split the connected component into a plurality of detached connected components. An optimal detaching parameter is determined representing a number of the detaching operations. A detached connected component resulting from performing the number of detaching operations corresponding to the optimal detaching parameter is classified as the structure of interest based on the probability map and the trained classifier.Type: ApplicationFiled: May 8, 2015Publication date: November 10, 2016Inventors: Nathan Lay, David Liu, Jan Kretschmer, Shaohua Kevin Zhou
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Publication number: 20160328841Abstract: Systems and methods for training a region clustering forest include receiving a set of medical training images for a population of patients. A set of image patches is extracted from each image in the set of medical training images. A plurality of region clustering trees are generated each minimizing a loss function based on respective randomly selected subsets of the set of image patches to train the region clustering forest. Each of the plurality of region clustering trees cluster image patches at a plurality of leaf nodes and the loss function measures a compactness of the cluster of image patches at each leaf node in each of the plurality of region clustering trees.Type: ApplicationFiled: May 8, 2015Publication date: November 10, 2016Inventors: Nathan Lay, Dong Yang, David Liu, Shaohua Kevin Zhou
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Publication number: 20160328855Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.Type: ApplicationFiled: May 4, 2015Publication date: November 10, 2016Inventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
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Publication number: 20160328643Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.Type: ApplicationFiled: May 7, 2015Publication date: November 10, 2016Inventors: David Liu, Nathan Lay, Shaohua Kevin Zhou, Jan Kretschmer, Hien Nguyen, Vivek Kumar Singh, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu
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Publication number: 20150379744Abstract: A visualization method is provided that allows for the unfolding of a human skeleton from a medical image scan and providing increased efficiency for interacting with the image scan and whole body bone reading from such scans. That is, a full head-to-toe unfolded skeleton view (e.g., a 2D unfolded view) is realized for improved visualization and diagnostic capabilities.Type: ApplicationFiled: May 7, 2015Publication date: December 31, 2015Inventors: Jan Kretschmer, Nathan Lay, Shaohua Kevin Zhou
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Patent number: 8837771Abstract: A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.Type: GrantFiled: February 26, 2013Date of Patent: September 16, 2014Assignee: Siemens AktiengesellschaftInventors: Nathan Lay, Neil Birkbeck, Jingdan Zhang, Jens Guehring, Shaohua Kevin Zhou
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Publication number: 20130223704Abstract: A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.Type: ApplicationFiled: February 26, 2013Publication date: August 29, 2013Applicants: Siemens Aktiengesellschaft, Siemens CorporationInventors: Nathan Lay, Neil Birkbeck, Jingdan Zhang, Jens Guehring, Shaohua Kevin Zhou