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).

  • Publication number: 20190271714
    Abstract: A model-based method of determining characteristics of a specimen container cap to identify the container cap. The method includes providing a specimen container including a container cap; capturing backlit images of the container cap taken at different exposures lengths and using a plurality of different nominal wavelengths; selecting optimally-exposed pixels from the images at different exposure lengths at each nominal wavelength to generate optimally-exposed image data for each nominal wavelength; classifying the optimally-exposed pixels as at least being one of a tube, a label or a cap; and identifying a shape of the container cap based upon the optimally-exposed pixels classified as being the cap and the image data for each nominal wavelength. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are numerous other aspects.
    Type: Application
    Filed: July 7, 2017
    Publication date: September 5, 2019
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack
  • Patent number: 10390754
    Abstract: A method and system for motion estimation modeling for cardiac and respiratory motion compensation is disclosed. Specifically, a coronary sinus catheter is tracked in a plurality of frames of a fluoroscopic image sequence; and cardiac and respiratory motion of a left atrium is estimated in each of the plurality of frames based on tracking results of the coronary sinus catheter using a trained motion estimation model.
    Type: Grant
    Filed: January 18, 2013
    Date of Patent: August 27, 2019
    Assignees: Siemens Healthcare GmbH, FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG
    Inventors: Alexander Benjamin Brost, Sebastian Kaeppler, Martin Ostermeier, Norbert Strobel, Wen Wu, Terrence Chen
  • Publication number: 20190223819
    Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.
    Type: Application
    Filed: January 24, 2018
    Publication date: July 25, 2019
    Inventors: Tommaso Mansi, Tiziano Passerini, Puneet Sharma, Terrence Chen, Ahmet Tuysuzoglu, Shun Miao, Alexander Brost
  • Publication number: 20190209098
    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: Application
    Filed: January 5, 2018
    Publication date: July 11, 2019
    Inventors: Lequan Yu, Kai Ma, Vivek Singh, Terrence Chen
  • Publication number: 20190214135
    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: Application
    Filed: January 11, 2018
    Publication date: July 11, 2019
    Inventors: Yifan Wu, Vivek Kumar Singh, Kai Ma, Terrence Chen, Birgi Tamersoy, Jiangping Wang, Andreas Krauss
  • Publication number: 20190213442
    Abstract: A method for training a learning-based medical scanner including (a) obtaining training data from demonstrations of scanning sequences, and (b) learning the medical scanner's control policies using deep reinforcement learning framework based on the training data.
    Type: Application
    Filed: January 10, 2018
    Publication date: July 11, 2019
    Inventors: Vivek Kumar Singh, Klaus J. Kirchberg, Kai Ma, Yao-jen Chang, Terrence Chen
  • Patent number: 10335115
    Abstract: Multi-source, multi-type image registration is provided. Images are received from a plurality of image devices, and images are received from a medical imaging device. A pre-existing diagram of a probe of the medical imaging device is received. A four-dimensional model is determined based on the received images from the image devices. A pose of the probe of the medical imaging device is determined based on the pre-existing diagram of the probe and the received images from the image devices. The plurality of images from the medical imaging device are registered with the four-dimensional model based on a common coordinate system and the determined pose of the probe.
    Type: Grant
    Filed: September 3, 2015
    Date of Patent: July 2, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Sasa Grbic, Tommaso Mansi, Stefan Kluckner, Charles Henri Florin, Terrence Chen, Dorin Comaniciu
  • Patent number: 10331850
    Abstract: A method for estimating a body surface model of a patient includes: (a) segmenting, by a computer processor, three-dimensional sensor image data to isolate patient data from environmental data; (b) categorizing, by the computer processor, a body pose of the patient from the patient data using a first trained classifier; (c) parsing, by the computer processor, the patient data to an anatomical feature of the patient using a second trained classifier, wherein the parsing is based on a result of the categorizing; and (d) estimating, by the computer processor, the body surface model of the patient based on a result of the parsing. Systems for estimating a body surface model of a patient are described.
    Type: Grant
    Filed: January 27, 2015
    Date of Patent: June 25, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Vivek Kumar Singh, Yao-jen Chang, Kai Ma, Terrence Chen, Michael Wels, Grzegorz Soza
  • Patent number: 10325182
    Abstract: Embodiments are directed to classifying barcode tag conditions on sample tubes from top view images to streamline sample tube handling in advanced clinical laboratory automation systems. The classification of barcode tag conditions leads to the automatic detection of problematic barcode tags, allowing for a user to take necessary steps to fix the problematic barcode tags. A vision system is utilized to perform the automatic classification of barcode tag conditions on sample tubes from top view images. The classification of barcode tag conditions on sample tubes from top view images is based on the following factors: (1) a region-of-interest (ROI) extraction and rectification method based on sample tube detection; (2) a barcode tag condition classification method based on holistic features uniformly sampled from the rectified ROI; and (3) a problematic barcode tag area localization method based on pixel-based feature extraction.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: June 18, 2019
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Khurram Soomro, Yao-Jen Chang, Stefan Kluckner, Wen Wu, Benjamin Pollack, Terrence Chen
  • Patent number: 10319092
    Abstract: A method for detecting properties of sample tubes is provided that includes extracting image patches substantially centered on a tube slot of a tray or a tube top in a slot. For each image patch, the method may include assigning a first location group defining whether the image patch is an image center, a corner of an image or a middle edge of an image, selecting a trained classifier based on the first location group and determining whether each tube slot contains a tube. The method may also include assigning a second location group defining whether the image patch is from an image center, a left corner of the image, a right corner of the image, a left middle of the image; a center middle of the image or a right middle of the image, selecting a trained classifier based on the second location group and determining a tube property.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: June 11, 2019
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Wen Wu, Benjamin Pollack, Yao-Jen Chang, Guillaume Dumont, Terrence Chen
  • Publication number: 20190142358
    Abstract: A method for generating a nuclear image includes obtaining, via a camera, a surface image of a patient. A synthetic computed-tomography (CT) image of the patient is generated based on the surface image. First time-of-flight (TOF) data for the patient is obtained via a nuclear imaging modality. Attenuation correction is applied to the first TOF data. The synthetic image is applied as a density map during the attenuation correction. A nuclear image is generated from the attenuation corrected first TOF data.
    Type: Application
    Filed: November 13, 2017
    Publication date: May 16, 2019
    Inventors: Terrence Chen, Vivek Kumar Singh, Klaus J. Kirchberg, Vladimir Y. Panin, Dorin Comaniciu
  • Patent number: 10290090
    Abstract: Embodiments provide a method of using image-based tube top circle detection that includes extracting, from one of a series of images of a tube tray, a region of interest (ROI) patch having a target tube top circle and boundaries constrained by two dimensional (2D) projections of different types of tube top circle centers. The method also includes calculating an edge gradient magnitude map of the ROI patch and constructing a three dimensional (3D) map of a circle parameter space, each location in the 3D map corresponding to a circle parameter having a center location and a diameter. The method further includes accumulating weighted votes in the 3D map from edge points in the edge gradient magnitude map along edge point gradient directions, determining locations in the 3D map as circle candidates based on the accumulated votes and selecting a target tube top circle based on the greatest accumulated votes.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: May 14, 2019
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Yao-Jen Chang, Wen Wu, Guillaume Dumont, Benjamin Pollack, Terrence Chen
  • Publication number: 20190139300
    Abstract: A method of deriving one or more medical scene model characteristics for use by one or more software applications is disclosed. The method includes receiving one or more sensor data streams. Each sensor data stream of the one or more sensor data steams includes position information relating to a medical scene. A medical scene model including a three-dimensional representation of a state of the medical scene is dynamically updated based on the one or more sensor data streams. Based on the medical scene model, the one or more medical scene model characteristics are derived.
    Type: Application
    Filed: November 7, 2018
    Publication date: May 9, 2019
    Inventors: Klaus J. Kirchberg, Vivek Kumar Singh, Terrence Chen
  • Publication number: 20190139259
    Abstract: A correspondence between frames of a set of medical image data is determined where the set of medical image data includes at least one frame acquired without contrast medium and at least one frame acquired with contrast medium. First data representing a first image frame acquired without contrast medium is received. Second data representing a second image frame acquired with contrast medium is received. A position of a feature of a medical device in the second image frame is determined at least partly on the basis of a position of the feature determined from the first image frame.
    Type: Application
    Filed: October 30, 2018
    Publication date: May 9, 2019
    Inventors: Liheng Zhang, Vivek Kumar Singh, Kai Ma, Terrence Chen
  • Patent number: 10282638
    Abstract: A probe pose is detected in fluoroscopy medical imaging. The pose of the probe through a sequence of fluoroscopic images is detected. The detection relies on an inference framework for visual tracking overtime. By applying visual tracking, the pose through the sequence is consistent or the pose at one time guides the detection of the probe at another time. Single frame drop-out of detection may be avoided. Verification using detection of the tip of the probe and/or weighting of possible detections by separate detection of markers on the probe may further improve the accuracy.
    Type: Grant
    Filed: July 22, 2016
    Date of Patent: May 7, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Shanhui Sun, Tobias Heimann, Shun Miao, Rui Liao, Terrence Chen
  • Publication number: 20190130603
    Abstract: Systems, methods, and computer-readable media are disclosed for determining feature representations of 2.5D image data using deep learning techniques. The 2.5D image data may be synthetic image data generated from 3D simulated model data such as 3D CAD data. The 2.5D image data may be indicative of any number of pose estimations/camera poses representing virtual or actual viewing perspectives of an object modeled by the 3D CAD data. A neural network such as a convolution neural network (CNN) may be trained using the 2.5D image data as training data to obtain corresponding feature representations. The pose estimations/camera poses may be stored in a data repository in association with the corresponding feature representations. The learnt CNN may then be used to determine an input feature representation from an input 2.5D image and index the input feature representation against the data repository to determine matching pose estimation(s).
    Type: Application
    Filed: March 9, 2017
    Publication date: May 2, 2019
    Inventors: Shanhui Sun, Kai Ma, Stefan Kluckner, Ziyan Wu, Jan Ernst, Vivek Kumar Singh, Terrence Chen
  • Patent number: 10268915
    Abstract: A method for real-time collimation and ROI-filter positioning in X-ray imaging in interventional procedures includes acquiring an image of a region-of-interest (ROI) at a beginning of a medical intervention procedure on a subject, classifying the image based on low-level features in the image to determine a type of procedure being performed, determining a list of landmarks in the image from the type of procedure being performed, and loading a pre-trained landmark model for each landmark in the list of landmarks, where landmarks include anatomical structures of the subject and medical devices being used in the medical intervention procedure, and computing collimator settings of an X-ray imaging device from ROI filter margins and bounding boxes of the landmarks calculated using the landmark models.
    Type: Grant
    Filed: June 9, 2015
    Date of Patent: April 23, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Wen Wu, Terrence Chen, Anton Nekovar, Martin Ostermeier, Dorin Comaniciu
  • Publication number: 20190102909
    Abstract: Systems, methods, and computer-readable media are disclosed for automated identification of parts of a parts assembly using image data of the parts assembly and 3D simulated model data of the parts assembly. The 3D simulated model data may be 3D CAD data of the parts assembly. An image of the parts assembly is captured by a mobile device and sent to a back-end server for processing. The back-end server determines a feature representation corresponding to the image and searches a repository to locate a matching feature representation stored in association with a corresponding pose estimation. The matching pose estimation is rendered as an overlay on the image of the parts assembly, thereby enabling automated identification of parts within the image or some user-selected portion of the image.
    Type: Application
    Filed: March 9, 2017
    Publication date: April 4, 2019
    Inventors: Stefan Kluckner, Shanhui Sun, Kai Ma, Ziyan Wu, Arun Innanje, Jan Ernst, Terrence Chen
  • Publication number: 20190080475
    Abstract: A method for identifying a feature in a first image comprises establishing an initial database of image triplets, and in a pose estimation processor, training a deep learning neural network using the initial database of image triplets, calculating a pose for the first image using the deep learning neural network, comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network, creating a new set of training data including a plurality of error cases identified in a plurality of input images and retraining the deep learning neural network using the new set of training data. The deep learning neural network may be iteratively retrained with a series of new training data sets. Statistical analysis is performed on a plurality of error cases to select a subset of the error cases included in the new set of training data.
    Type: Application
    Filed: March 13, 2017
    Publication date: March 14, 2019
    Inventors: Kai Ma, Shanhui Sun, Stefan Kluckner, Ziyan Wu, Terrence Chen, Jan Ernst
  • Publication number: 20190057521
    Abstract: For topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates the topogram representing an interior organ based on the outside surface of the patient. To further adapt to specific patients, internal landmarks are used in the topogram prediction. The topogram generated by one generator of the GAN may be altered based on landmarks generated by another generator.
    Type: Application
    Filed: July 20, 2018
    Publication date: February 21, 2019
    Inventors: Brian Teixeira, Vivek Kumar Singh, Birgi Tamersoy, Terrence Chen, Kai Ma, Andreas Krauss