Patents by Inventor Terrence Chen

Terrence Chen 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).

  • Patent number: 10635924
    Abstract: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.
    Type: Grant
    Filed: May 11, 2015
    Date of Patent: April 28, 2020
    Inventors: Ali Kamen, Shanhui Sun, Terrence Chen, Tommaso Mansi, Alexander Michael Gigler, Patra Charalampaki, Maximillian Fleischer, Dorin Comaniciu
  • Patent number: 10624602
    Abstract: Embodiments include a medical imaging device and a method controlling one or more parameters of a medical imaging device. In one embodiment, a method includes receiving image data representing a first image of an object to be imaged using the radiation source and detecting a plurality of positions of respective predetermined features in the first image. Based upon the detected positions, a boundary of an imaging area of the object to be imaged is determined. Based on the determined boundary, one or more parameters of the radiation source unit are controlled.
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: April 21, 2020
    Assignee: SIEMENS HEALTHCARE GMBH
    Inventors: Yao-jen Chang, Terrence Chen, Birgi Tamersoy, Vivek Kumar Singh, Susanne Oepping, Ralf Nanke
  • Patent number: 10610181
    Abstract: Robust calcification tracking is provided in fluoroscopic imagery. A patient with an inserted catheter is scanned over time. A processor detects the catheter in the patient from the scanned image data. The processor tracks the movement of the catheter. The processor also detects a structure represented in the data. The structure is detected as a function of movement with a catheter. The processor tracks the movement of the structure using sampling based on a previous location of the structure in the patient. The processor may output an image of the structure.
    Type: Grant
    Filed: February 12, 2016
    Date of Patent: April 7, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Terrence Chen, Sarfaraz Hussein, Matthias John, Vivek Kumar Singh
  • Publication number: 20200074296
    Abstract: A trained recurrent neural network having a set of control policies learned from application of a template dataset and one or more corresponding template deep network architectures may generate a deep network architecture for performing a task on an application dataset. The template deep network architectures may have an established level or performance in executing the task. A deep network based on the deep network architecture may trained to perform the task on the application dataset. The control policies of the recurrent neural network may be updated based on the performance of the trained deep network.
    Type: Application
    Filed: September 5, 2018
    Publication date: March 5, 2020
    Inventors: Vivek Kumar Singh, Terrence Chen, Dorin Comaniciu
  • Publication number: 20200057778
    Abstract: In pose estimation from a depth sensor (12), depth information is matched (70) with 3D information. Depending on the shape captured in depth image information, different objects may benefit from more or less pose density from different perspectives. The database (48) is created by bootstrap aggregation (64). Possible additional poses are tested (70) for nearest neighbors already in the database (48). Where the nearest neighbor is far, then the additional pose is added (72). Where the nearest neighbor is not far, then the additional pose is not added. The resulting database (48) includes entries for poses to distinguish the pose without overpopulation. The database (48) is indexed and used to efficiently determine pose from a depth camera (12) of a given captured image.
    Type: Application
    Filed: April 11, 2017
    Publication date: February 20, 2020
    Inventors: Shanhui Sun, Stefan Kluckner, Ziyan Wu, Oliver Lehmann, Jan Ernst, Terrence Chen
  • Publication number: 20200057831
    Abstract: The present embodiments relate to generating synthetic depth data. By way of introduction, the present embodiments described below include apparatuses and methods for modeling the characteristics of a real-world light sensor and generating realistic synthetic depth data accurately representing depth data as if captured by the real-world light sensor. To generate accurate depth data, a sequence of procedures are applied to depth images rendered from a three-dimensional model. The sequence of procedures simulate the underlying mechanism of the real-world sensor. By simulating the real-world sensor, parameters relating to the projection and capture of the sensor, environmental illuminations, image processing and motion are accurately modeled for generating depth data.
    Type: Application
    Filed: February 23, 2017
    Publication date: February 20, 2020
    Inventors: Ziyan Wu, Shanhui Sun, Stefan Kluckner, Terrence Chen, Jan Ernst
  • Publication number: 20200051257
    Abstract: Imaging from sequential scans is aligned based on patient information. A three-dimensional distribution of a patient-related object or objects, such as an outer surface of the patient or an organ in the patient, is stored with any results (e.g., images and/or measurements). Rather than the entire scan volume, the three-dimensional distributions from the different scans are used to align between the scans. The alignment allows diagnostically useful comparison between the scans, such as guiding an imaging technician to more rapidly determine the location of a same lesion for size comparison.
    Type: Application
    Filed: August 8, 2018
    Publication date: February 13, 2020
    Inventors: Frank Sauer, Shelby Scott Brunke, Andrzej Milkowski, Ali Kamen, Ankur Kapoor, Mamadou Diallo, Terrence Chen, Klaus J. Kirchberg, Vivek Kumar Singh, Dorin Comaniciu
  • Publication number: 20200013189
    Abstract: The present embodiments relate to automatically estimating a three]dimensional pose of an object from an image captured using a camera with a structured light sensor. By way of introduction, the present embodiments described below include apparatuses and methods for training a system for and estimating a pose of an object from a test image. Training and test images are sampled to generate local image patches. Features are extracted from the local image patches to generate feature databased used to estimate nearest neighbor poses for each local image patch. The closest nearest neighbor pose to the test image is selected as the estimated three]dimensional pose.
    Type: Application
    Filed: February 23, 2017
    Publication date: January 9, 2020
    Inventors: Srikrishna Karanam, Ziyan Wu, Shanhui Sun, Oliver Lehmann, Stefan Kluckner, Terrence Chen, Jan Ernst
  • Patent number: 10521927
    Abstract: Machine learning is used to train a network to predict the location of an internal body marker from surface data. A depth image or other image of the surface of the patient is used to determine the locations of anatomical landmarks. The training may use a loss function that includes a term to limit failure to predict a landmark and/or off-centering of the landmark. The landmarks may then be used to configure medical scanning and/or for diagnosis.
    Type: Grant
    Filed: August 7, 2018
    Date of Patent: December 31, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Brian Teixeira, Vivek Kumar Singh, Birgi Tamersoy, Terrence Chen, Kai Ma, Andreas Krauss, Andreas Wimmer
  • Patent number: 10506984
    Abstract: A system and method includes acquisition of a plurality of sets of body surface data, first data indicating locations of a first one or more body landmarks for each of the plurality of sets of body surface data, and second data indicating locations of a second one or more body landmarks for each of the plurality of sets of body surface data, training, using the plurality of sets of body surface data and data indicating locations of the first one or more body landmarks for each of the plurality of sets of body surface data, of a first reinforcement learning network to identify the first one or more body landmarks based on body surface data, and training, using the plurality of sets of body surface data and data indicating locations of the second one or more body landmarks for each of the plurality of sets of body surface data, of a second reinforcement learning network to identify the second one or more body landmarks based on body surface data.
    Type: Grant
    Filed: January 5, 2018
    Date of Patent: December 17, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Lequan Yu, Kai Ma, Vivek Singh, Terrence Chen
  • Patent number: 10507002
    Abstract: A system includes: a movable X-ray tube scanner; a range sensor movable with the X-ray tube scanner; an X-ray detector positioned to detect X-rays from the X-ray tube passing through a standing subject between the X-ray tube and the X-ray detector; and a processor configured for automatically controlling the X-ray tube scanner to transmit X-rays to a region of interest of the patient while the subject is standing between the X-ray tube and the X-ray detector.
    Type: Grant
    Filed: May 23, 2017
    Date of Patent: December 17, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Vivek Kumar Singh, Yao-jen Chang, Birgi Tamersoy, Kai Ma, Susanne Oepping, Ralf Nanke, Terrence Chen
  • Patent number: 10478149
    Abstract: A method and a system for automatically aligning a positionable X-ray source of an X-ray system in alignment with a mobile X-ray detector is disclosed where the X-ray system detects the position of the mobile X-ray detector using a 3D camera and then driving the positionable X-ray source to a position in alignment with the mobile X-ray detector.
    Type: Grant
    Filed: February 21, 2017
    Date of Patent: November 19, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Birgi Tamersoy, Vivek Kumar Singh, Yao-jen Chang, Susanne Dornberger, Ralf Nanke, Terrence Chen
  • Patent number: 10482313
    Abstract: A method and system for classification of endoscopic images is disclosed. An initial trained deep network classifier is used to classify endoscopic images and determine confidence scores for the endoscopic images. The confidence score for each endoscopic image classified by the initial trained deep network classifier is compared to a learned confidence threshold. For endoscopic images with confidence scores higher than the learned threshold value, the classification result from the initial trained deep network classifier is output. Endoscopic images with confidence scores lower than the learned confidence threshold are classified using a first specialized network classifier built on a feature space of the initial trained deep network classifier.
    Type: Grant
    Filed: September 29, 2016
    Date of Patent: November 19, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Venkatesh N. Murthy, Vivek Kumar Singh, Shanhui Sun, Subhabrata Bhattacharya, Kai Ma, Ali Kamen, Bogdan Georgescu, Terrence Chen, Dorin Comaniciu
  • Patent number: 10475538
    Abstract: A system and method includes operation of a generation network to generate first generated computed tomography data based on a first instance of surface data, determination of a generation loss based on the first generated computed tomography data and on a first instance of computed tomography data which corresponds to the first instance of surface data, operation of a discriminator network to discriminate between the first generated computed tomography data and the first instance of computed tomography data, determination of a discriminator loss based on the discrimination between the first generated computed tomography data and the first instance of computed tomography data, determination of discriminator gradients of the discriminator network based on the discriminator loss, and updating of weights of the generation network based on the generation loss and the discriminator gradients.
    Type: Grant
    Filed: January 11, 2018
    Date of Patent: November 12, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Yifan Wu, Vivek Kumar Singh, Kai Ma, Terrence Chen, Birgi Tamersoy, Jiangping Wang, Andreas Krauss
  • Publication number: 20190318497
    Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.
    Type: Application
    Filed: April 11, 2018
    Publication date: October 17, 2019
    Inventors: Zhuokai Zhao, Yao-jen Chang, Ruhan Sa, Kai Ma, Jianping Wang, Vivek Kumar Singh, Terrence Chen, Andreas Wimmer, Birgi Tamersoy
  • Patent number: 10444406
    Abstract: A method for predicting short-term cloud coverage includes a computer calculating an estimated cloud velocity field at a current time value based on sky images. The computer determines a segmented cloud model based on the sky images, a future sun location corresponding to a future time value, and sun pixel locations at the future time value based on the future sun location. Next, the computer applies a back-propagation algorithm to the sun pixel locations using the estimated cloud velocity field to yield propagated sun pixel locations corresponding to a previous time value. Then, the computer predicts cloud coverage for the future sun location based on the propagated sun pixel locations and the segmented cloud model.
    Type: Grant
    Filed: April 17, 2014
    Date of Patent: October 15, 2019
    Assignee: Siemens Aktiengesellschaft
    Inventors: Shanhui Sun, Jan Ernst, Archana Sapkota, Eberhard Ritzhaupt-Kleissl, Jeremy Ralph Wiles, Terrence Chen
  • Patent number: 10430551
    Abstract: In scan data retrieval, a mesh is fit (32) to surface data of a current patient, such as data from an optical or depth sensor (18). Meshes are also fit (48) to medical scan data, such as fitting (48) to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared (34) to identify (36) a previous patient with similar body shape and/or size. The scan configuration (38) for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching (34) includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.
    Type: Grant
    Filed: November 6, 2015
    Date of Patent: October 1, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Jiangping Wang, Kai Ma, Vivek Singh, Mingqing Chen, Yao-Jen Chang, Shaohua Kevin Zhou, Terrence Chen, Andreas Krauss
  • Publication number: 20190290236
    Abstract: A method is disclosed for adjusting a collimator of an X-ray source. In an embodiment, the method includes detecting an arrangement of an X-ray detector with respect to the X-ray source; automatically determining an adjustment for the collimator based on the detected position of the X-ray detector with respect to the X-ray source; and automatically adjusting the collimator based on the determined adjustment for the collimator. An X-ray device and computer readable medium are also disclosed.
    Type: Application
    Filed: March 19, 2019
    Publication date: September 26, 2019
    Applicant: Siemens Healthcare GmbH
    Inventors: Susanne OEPPING, Ralf NANKE, Michael FUHRMANN, Birgi TAMERSOY, Yao-jen CHANG, Terrence CHEN
  • Patent number: 10425629
    Abstract: A system and method includes generation of a first map of first descriptors based on pixels of a first two-dimensional depth image, where a location of each first descriptor in the first map corresponds to a location of a respective pixel of a first two-dimensional depth image, generation of a second map of second descriptors based on pixels of the second two-dimensional depth image, where a location of each second descriptor in the second map corresponds to a location of a respective pixel of the second two-dimensional depth image, upsampling of the first map of descriptors using a first upsampling technique to generate an upsampled first map of descriptors, upsampling of the second map of descriptors using a second upsampling technique to generate an upsampled second map of descriptors, generation of a descriptor difference map based on differences between descriptors of the upsampled first map of descriptors and descriptors of the upsampled second map of descriptors, generation of a geodesic preservation m
    Type: Grant
    Filed: June 28, 2017
    Date of Patent: September 24, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Vivek Kumar Singh, Stefan Kluckner, Yao-jen Chang, Kai Ma, Terrence Chen
  • Publication number: 20190277870
    Abstract: A method of characterizing a specimen for HILN (H, I, and/or L, or N). The method includes capturing images of the specimen at multiple different viewpoints, processing the images to provide segmentation information for each viewpoint, generating a semantic map from the segmentation information, selecting a synthetic viewpoint, identifying front view semantic data and back view semantic data for the synthetic viewpoint, and determining HILN of the serum or plasma portion based on the front view semantic data with an HILN classifier, while taking into account back view semantic data. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
    Type: Application
    Filed: November 13, 2017
    Publication date: September 12, 2019
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Shanhui Sun, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack