Patents by Inventor Shaohua Kevin Zhou

Shaohua Kevin Zhou 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).

  • Method and system for approximating deep neural networks for anatomical object detection
    Patent number: 9633306
    Abstract: 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: Grant
    Filed: May 7, 2015
    Date of Patent: April 25, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: David Liu, Nathan Lay, Shaohua Kevin Zhou, Jan Kretschmer, Hien Nguyen, Vivek Kumar Singh, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu
  • Intelligent Medical Image Landmark Detection
    Publication number: 20170103532
    Abstract: Intelligent image parsing for anatomical landmarks and/or organs detection and/or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.
    Type: Application
    Filed: December 21, 2016
    Publication date: April 13, 2017
    Inventors: Florin Cristian Ghesu, Bogdan Georgescu, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Method and system for volume rendering based 3D image filtering and real-time cinematic rendering
    Patent number: 9613452
    Abstract: A method and apparatus for volume rendering based 3D image filtering and real-time cinematic volume rendering is disclosed. A set of 2D projection images of the 3D volume is generated using cinematic volume rendering. A reconstructed 3D volume is generated from the set of 2D projection images using an inverse linear volumetric ray tracing operator. The reconstructed 3D volume inherits noise suppression and structure enhancement from the projection images generated using cinematic rendering, and is thus non-linearly filtered. Real-time volume rendering can be performed on the reconstructed 3D volume using volumetric ray tracing, and each projected image of the reconstructed 3D volume is an approximation of a cinematic rendered image of the original volume.
    Type: Grant
    Filed: March 9, 2015
    Date of Patent: April 4, 2017
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Shaohua Kevin Zhou, Klaus Engel
  • Method and system for medical image synthesis across image domain or modality using iterative sparse representation propagation
    Patent number: 9595120
    Abstract: A method and apparatus for medical image synthesis across image modalities or domains is disclosed, which synthesizes a target medical image based on a source medical image. A plurality of image patches are cropped from the source medical image. A synthesized target medical image is then generated from the source medical image by jointly performing sparse coding between each image patch of the source medical image and a corresponding image patch of the synthesized target image based on jointly trained source and target dictionaries.
    Type: Grant
    Filed: April 27, 2015
    Date of Patent: March 14, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Hien Nguyen, Shaohua Kevin Zhou
  • Learning-based aorta segmentation using an adaptive detach and merge algorithm
    Patent number: 9589211
    Abstract: 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: Grant
    Filed: May 8, 2015
    Date of Patent: March 7, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Nathan Lay, David Liu, Jan Kretschmer, Shaohua Kevin Zhou
  • Histological differentiation grade prediction of hepatocellular carcinoma in computed tomography images
    Patent number: 9585627
    Abstract: A method of determining the histological grade of Hepatocellular Carcinoma (HCC) including: acquiring a Computed Tomography (CT) image of a person including an HCC tumor; delineating the HCC tumor; and assigning a histological grade to the HCC tumor, wherein assigning the histological grade to the HCC tumor includes: applying a plurality of filters to the HCC tumor, wherein each of the filters produces a corresponding response image and, for each of the filters, a convolution operation is performed on the filter and the CT image to produce the response image corresponding to that filter; computing an average response of the HCC tumor in each of the response images and recording each of the average responses as an Independent Subspace Analysis (ISA) feature; and determining the histological grade of the HCC tumor based on the ISA features by using a classifier.
    Type: Grant
    Filed: August 7, 2014
    Date of Patent: March 7, 2017
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: David Liu, Shaohua Kevin Zhou
  • SEMANTIC CINEMATIC VOLUME RENDERING
    Publication number: 20170061672
    Abstract: 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: Application
    Filed: September 1, 2015
    Publication date: March 2, 2017
    Inventors: Shaohua Kevin Zhou, Klaus Engel, David Liu, Daphne Yu, Bernhard Geiger, Nathan Lay
  • Method and system for efficient extraction of a silhouette of a 3D mesh
    Patent number: 9582934
    Abstract: A method and system for extracting a silhouette of a 3D mesh representing an anatomical structure is disclosed. The 3D mesh is projected to two dimensions. Silhouette candidate edges are generated in the projected mesh by pruning edges and mesh points based on topology analysis of the projected mesh. Each silhouette candidate edge that intersects with another edge in the projected mesh is split into two silhouette candidate edges. The silhouette is extracted using an edge following process on the silhouette candidate edges.
    Type: Grant
    Filed: September 19, 2011
    Date of Patent: February 28, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Yefeng Zheng, Yu Pang, Rui Liao, Matthias John, Jan Boese, Shaohua Kevin Zhou, Dorin Comaniciu
  • Method and system for unsupervised cross-modal medical image synthesis
    Patent number: 9582916
    Abstract: A method and apparatus for unsupervised cross-modal medical image synthesis is disclosed, which synthesizes a target modality medical image based on a source modality medical image without the need for paired source and target modality training data. A source modality medical image is received. Multiple candidate target modality intensity values are generated for each of a plurality of voxels of a target modality medical image based on corresponding voxels in the source modality medical image. A synthesized target modality medical image is generated by selecting, jointly for all of the plurality of voxels in the target modality medical image, intensity values from the multiple candidate target modality intensity values generated for each of the plurality of voxels. The synthesized target modality medical image can be refined using coupled sparse representation.
    Type: Grant
    Filed: September 30, 2015
    Date of Patent: February 28, 2017
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Raviteja Vemulapalli, Hien Nguyen, Shaohua Kevin Zhou
  • Region clustering forest for analyzing medical imaging data
    Patent number: 9576356
    Abstract: 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: Grant
    Filed: May 8, 2015
    Date of Patent: February 21, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Nathan Lay, Dong Yang, David Liu, Shaohua Kevin Zhou
  • Intelligent medical image landmark detection
    Patent number: 9569736
    Abstract: Intelligent image parsing for anatomical landmarks and/or organs detection and/or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.
    Type: Grant
    Filed: May 20, 2016
    Date of Patent: February 14, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Florin Cristian Ghesu, Bogdan Georgescu, Dominik Neumann, Tommaso Mansi, Dorin Comaniciu, Wen Liu, Shaohua Kevin Zhou
  • Visualization method for a human skeleton from a medical scan
    Patent number: 9558568
    Abstract: 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: Grant
    Filed: May 7, 2015
    Date of Patent: January 31, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Jan Kretschmer, Nathan Lay, Shaohua Kevin Zhou
  • System and method for data driven editing of rib unfolding
    Patent number: 9547906
    Abstract: A method and apparatus for data driven editing of rib centerlines is disclosed. A user input location indicating an inaccuracy in a rib centerline extracted from a medical image volume is received. A local correction of the rib centerline is performed. A portion of the rib surrounding a current centerline point to be corrected is segmented based on image data of the medical image volume. A corrected centerline point for the current centerline point is generated based on the segmented portion of the rib. The centerline correction is then extended to subsequent points along the rib centerline.
    Type: Grant
    Filed: September 18, 2014
    Date of Patent: January 17, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Noha Youssry El-Zehiry, Grzegorz Soza, Andreas Wimmer, Shaohua Kevin Zhou
  • Method and system of deterministic fatigue life prediction for rotor materials
    Patent number: 9541530
    Abstract: A method of fatigue life prediction including: calculating a critical crack size of an object of interest; identifying a first flaw in ultrasound data of the object of interest; determining that the first flaw interacts with a second flaw, the first flaw is to be merged with the second flaw, or the first flaw is isolated; calculating an initial crack size based on the determination; and calculating an increase in the initial crack size due to fatigue and creep to determine a number of load cycles until the initial crack size reaches the critical crack size.
    Type: Grant
    Filed: January 17, 2013
    Date of Patent: January 10, 2017
    Assignee: Siemens Energy, Inc.
    Inventors: Xuefei Guan, Hui Zhen, Jingdan Zhang, Shaohua Kevin Zhou, Ashley L. Lewis, Steve H. Radke, Chin-Sheng Lee
  • Method and system for automatic pelvis unfolding from 3D computed tomography images
    Patent number: 9542741
    Abstract: A method and system for automatic pelvis unfolding from 3D computed tomography (CT) images is disclosed. A 3D medical image, such as a 3D CT image, is received. Pelvis anatomy is segmented in the 3D medical image. The 3D medical image is reformatted to visualize an unfolded pelvis based on the segmented pelvis anatomy.
    Type: Grant
    Filed: February 12, 2014
    Date of Patent: January 10, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Neil Birkbeck, Dijia Wu, Michal Sofka, Meizhu Liu, Grzegorz Soza, Shaohua Kevin Zhou, Clifford R. Weiss, Atilla Peter Kiraly
  • Multi-bone segmentation for 3D computed tomography
    Patent number: 9495752
    Abstract: Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.
    Type: Grant
    Filed: July 30, 2013
    Date of Patent: November 15, 2016
    Assignee: Siemens Product Lifecycle Management Software Inc.
    Inventors: Dijia Wu, Neil Birkbeck, Michal Sofka, Meizhu Liu, Shaohua Kevin Zhou
  • Learning-Based Aorta Segmentation using an Adaptive Detach and Merge Algorithm
    Publication number: 20160328631
    Abstract: 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: Application
    Filed: May 8, 2015
    Publication date: November 10, 2016
    Inventors: Nathan Lay, David Liu, Jan Kretschmer, Shaohua Kevin Zhou
  • Region Clustering Forest for Analyzing Medical Imaging Data
    Publication number: 20160328841
    Abstract: 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: Application
    Filed: May 8, 2015
    Publication date: November 10, 2016
    Inventors: Nathan Lay, Dong Yang, David Liu, Shaohua Kevin Zhou
  • Method and System for Whole Body Bone Removal and Vascular Visualization in Medical Image Data
    Publication number: 20160328855
    Abstract: 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: Application
    Filed: May 4, 2015
    Publication date: November 10, 2016
    Inventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
  • Method and System for Approximating Deep Neural Networks for Anatomical Object Detection
    Publication number: 20160328643
    Abstract: 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: Application
    Filed: May 7, 2015
    Publication date: November 10, 2016
    Inventors: David Liu, Nathan Lay, Shaohua Kevin Zhou, Jan Kretschmer, Hien Nguyen, Vivek Kumar Singh, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu