Patents by Inventor Yefeng Zheng

Yefeng Zheng 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).

  • Publication number: 20160106321
    Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.
    Type: Application
    Filed: April 13, 2015
    Publication date: April 21, 2016
    Inventors: Puneet Sharma, Ali Kamen, Bogdan Georgescu, Frank Sauer, Dorin Comaniciu, Yefeng Zheng, Hien Nguyen, Vivek Kumar Singh
  • Publication number: 20160093048
    Abstract: The present embodiments relate to machine learning for multimodal image data. By way of introduction, the present embodiments described below include apparatuses and methods for learning a similarity metric using deep learning based techniques for multimodal medical images. A novel similarity metric for multi-modal images is provided using the corresponding states of pairs of image patches to generate a classification setting for each pair. The classification settings are used to train a deep neural network via supervised learning. A multi-modal stacked denoising auto encoder (SDAE) is used to pre-train the neural network. A continuous and smooth similarity metric is constructed based on the output of the neural network before activation in the last layer. The trained similarity metric may be used to improve the results of image fusion.
    Type: Application
    Filed: September 25, 2015
    Publication date: March 31, 2016
    Inventors: Xi Cheng, Li Zhang, Yefeng Zheng
  • Patent number: 9275190
    Abstract: A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention.
    Type: Grant
    Filed: April 9, 2008
    Date of Patent: March 1, 2016
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Yefeng Zheng, Adrian Barbu, Bogdan Georgescu, Michael Lynch, Michael Scheuering, Dorin Comaniciu
  • Publication number: 20160045180
    Abstract: A pair of medical images is analysed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier.
    Type: Application
    Filed: August 18, 2014
    Publication date: February 18, 2016
    Inventors: Michael Kelm, Yefeng Zheng
  • Publication number: 20160048741
    Abstract: Object detection uses a deep or multiple layer network to learn features for detecting the object in the image. Multiple features from different layers are aggregated to train a classifier for the object. In addition or as an alternative to feature aggregation from different layers, an initial layer may have separate learnt nodes for different regions of the image to reduce the number of free parameters. The object detection is learned or a learned object detector is applied.
    Type: Application
    Filed: August 12, 2014
    Publication date: February 18, 2016
    Inventors: Hien Nguyen, Vivek Kumar Singh, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu, Shaohua Kevin Zhou
  • Publication number: 20150359601
    Abstract: Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.
    Type: Application
    Filed: May 7, 2015
    Publication date: December 17, 2015
    Inventors: Frank Sauer, Yefeng Zheng, Puneet Sharma, Bogdan Georgescu
  • Patent number: 9147268
    Abstract: Background information is subtracted from projection data in medical diagnostic imaging. The background is removed using data acquired in a single rotational sweep of a C-arm. The removal may be by masking out a target, leaving the background, in the data as constructed into a volume. For subtraction, the masked background information is projected to a plane and subtracted from the data representing the plane.
    Type: Grant
    Filed: June 26, 2012
    Date of Patent: September 29, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Mingqing Chen, Yefeng Zheng, Kerstin Mueller, Christopher Rohkohl, G√ľnter Lauritsch, Jan Boese, Gareth Funka-Lea, Dorin Comaniciu
  • Patent number: 9129417
    Abstract: A method and system for extracting coronary artery centerlines from 3D medical image volumes is disclosed. Heart chambers are segmented in a 3D volume. Coronary artery centerlines are initialized in the 3D volume coronary artery based on the segmented heart chambers. The coronary artery centerlines are locally refined based on a vesselness measure. A length of each coronary artery centerline is shrunk to verify that the coronary artery centerline is within a coronary artery. The coronary artery centerline is the extended using data-driven vessel tracing.
    Type: Grant
    Filed: November 5, 2012
    Date of Patent: September 8, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Yefeng Zheng, Jianhua Shen, Huseyin Tek, Gareth Funka-Lea, Fernando Vega-Higuera, Dorin Comaniciu
  • Publication number: 20150238148
    Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.
    Type: Application
    Filed: May 12, 2015
    Publication date: August 27, 2015
    Inventors: Bogdan Georgescu, Yefeng Zheng, Hien Nguyen, Vivek Kumar Singh, Dorin Comaniciu, David Liu
  • Publication number: 20150235360
    Abstract: The coronary sinus or other vessel is segmented by finding a centerline and then using the centerline to locate the boundary of the vessel. For finding the centerline, a refinement process uses multi-scale sparse appearance learning. For locating the boundary, the lumen is segmented as a graph cut problem.
    Type: Application
    Filed: February 6, 2015
    Publication date: August 20, 2015
    Inventors: Yefeng Zheng, Shiyang Lu, Xiaojie Huang
  • Patent number: 9042618
    Abstract: A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.
    Type: Grant
    Filed: June 7, 2010
    Date of Patent: May 26, 2015
    Assignee: Siemens Aktiengesellshaft
    Inventors: Michael Kelm, Shaohua Kevin Zhou, Yefeng Zheng, Michael Suehling
  • Patent number: 9042619
    Abstract: A method and system for detection of native and bypass coronary ostia in a 3D volume, such as a CT volume, is disclosed. Native coronary ostia are detected by detecting a bounding box defining locations of a left native coronary ostium and a right native coronary ostium in the 3D volume using marginal space learning (MSL), and locally refining the locations of the left native coronary ostium and the right native coronary ostium using a trained native coronary ostium detector. Bypass coronary ostia are detected by segmenting an ascending aorta surface mesh in the 3D volume, generating a search region of a plurality of mesh points on the ascending aorta surface mesh based on a distribution of annotated bypass coronary ostia in a plurality of training volumes, and detecting the bypass coronary ostia by searching the plurality of mesh points in the search region.
    Type: Grant
    Filed: September 15, 2011
    Date of Patent: May 26, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Yefeng Zheng, Fernando Vega-Higuera, Shaohua Kevin Zhou, Dorin Comaniciu
  • Patent number: 9020227
    Abstract: A computer-implemented method of determining an interatrial septum ring in a cardiac image includes determining a left atrium mean shape based on a plurality of training images and determining an interatrial septum ring mean shape based on the left atrium mean shape. A left atrium mesh is identified in a new image. Then, a deformation field from the left atrium mean shape to the left atrium mesh is calculated and applied to the interatrial septum ring mean shape to determine the interatrial septum ring in the new image.
    Type: Grant
    Filed: June 13, 2013
    Date of Patent: April 28, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Yefeng Zheng, Matthias John
  • Publication number: 20150112182
    Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.
    Type: Application
    Filed: October 16, 2014
    Publication date: April 23, 2015
    Inventors: Puneet Sharma, Ali Kamen, Bogdan Georgescu, Frank Sauer, Dorin Comaniciu, Yefeng Zheng, Hien Nguyen, Vivek Kumar Singh
  • Patent number: 9014423
    Abstract: A method and system for adaptive discriminant learning and measurement fusion for image based catheter tracking is disclosed. An adaptive discriminant model is trained online based on a tracked object, such as a pigtail catheter tip, in at least one previous frame of a fluoroscopic image sequence. The object is tracked in the current frame of the fluoroscopic image sequence based at least on the adaptive discriminant model trained online. The object may be tracked in the current frame based on a fusion of three types of measurement models including the adaptive discriminant model trained online, an object detection model trained offline, and an online appearance model.
    Type: Grant
    Filed: March 6, 2012
    Date of Patent: April 21, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Peng Wang, Yefeng Zheng, Matthias John, Jan Boese, Gareth Funka-Lea, Dorin Comaniciu
  • Patent number: 9014449
    Abstract: A method and system for segmentation and removal of pulmonary arteries, pulmonary veins, and a left atrial appendage from 3D medical image data, such as 3D computed tomography (CT) volumes, is disclosed. A global shape model is segmented for each of pulmonary arteries, pulmonary veins, and a left atrial appendage in a 3D volume. The segmented global shape model for each of the pulmonary arteries, pulmonary veins, and left atrial appendage is locally refined based in local voxel intensities in the 3D volume, resulting in a respective mask for each structure. The mask is used to remove voxels belonging to the pulmonary arteries, pulmonary veins, and left atrial appendage from the 3D volume in order to better visualize coronary arteries and bypass arteries.
    Type: Grant
    Filed: September 28, 2012
    Date of Patent: April 21, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Hua Zhong, Yefeng Zheng, Gareth Funka-Lea, Fernando Vega-Higuera, Dorin Comaniciu
  • Patent number: 9002078
    Abstract: A system and method for performing shape-constrained aortic valve landmark detection using 3D medical images is provided. A rigid global shape defining initial positions of a plurality of aortic valve landmarks is detected within a 3D image. Each of the plurality of aortic valve landmarks is detected based on the initial positions.
    Type: Grant
    Filed: September 8, 2010
    Date of Patent: April 7, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Yefeng Zheng, Matthias John, Jan Boese, Dorin Comaniciu
  • Patent number: 8923590
    Abstract: A method and system for estimating 3D cardiac motion from a single C-arm angiography scan is disclosed. An initial 3D volume is reconstructed from a plurality of 2D projection images acquired in a single C-arm scan. A static mesh is extracted by segmenting an object in the initial 3D volume. The static mesh is projected to each of the 2D projection images. A cardiac phase is determined for each of the 2D projection images. A deformed mesh is generated for each of a plurality of cardiac phases based on a 2D contour of the object and the projected mesh in each of the 2D projection images of that cardiac phase.
    Type: Grant
    Filed: January 10, 2012
    Date of Patent: December 30, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Mingqing Chen, Yefeng Zheng, Gareth Funka-Lea, Guenter Lauritsch, Jan Boese, Dorin Comaniciu
  • Publication number: 20140369576
    Abstract: A computer-implemented method of determining an interatrial septum ring in a cardiac image includes determining a left atrium mean shape based on a plurality of training images and determining an interatrial septum ring mean shape based on the left atrium mean shape. A left atrium mesh is identified in a new image. Then, a deformation field from the left atrium mean shape to the left atrium mesh is calculated and applied to the interatrial septum ring mean shape to determine the interatrial septum ring in the new image.
    Type: Application
    Filed: June 13, 2013
    Publication date: December 18, 2014
    Applicants: SIEMENS AKTIENGESELLSCHAFT, SIEMENS CORPORATION
    Inventors: Yefeng Zheng, Matthias John
  • Publication number: 20140355854
    Abstract: A method and a segmentation system are disclosed. An embodiment of the method includes providing an image representation of the structure; providing a start surface model, including a mesh with a plurality of vertices connected by edges; defining for each vertex a ray normal to the surface model at the position of the vertex; assigning more than two labels to each vertex, each label representing a candidate position of the vertex on the ray; providing a representation of likelihoods for each candidate position the likelihood referring to whether the candidate position corresponds to a surface point of the structure in the image representation; and defining a first order Markow Random Field with discrete multivariate random variables, the random variables including the labels of the candidate positions and the representation of likelihoods, finding an optimal segmentation of the structure by using an maximum a posteriori estimation in this Markow Random Field.
    Type: Application
    Filed: January 7, 2014
    Publication date: December 4, 2014
    Applicant: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Michael KELM, Felix LUGAUER, Jingdan ZHANG, Yefeng ZHENG