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: 10887558
    Abstract: Methods and systems for automatically setting up a sensor connected to an apparatus. For example, a computer-implemented method for automatically setting up a sensor connected to an apparatus includes: receiving a sensor-connection signal corresponding to a connection established between the sensor and the apparatus; determining whether a streaming microservice corresponding to the sensor has been downloaded onto the apparatus; if the streaming microservice has not been downloaded onto the apparatus, determining whether the streaming microservice corresponding to the sensor is supported by the apparatus; if the streaming microservice is supported by the apparatus, downloading a streaming microservice docker from a docker registry, the streaming microservice docker including the streaming microservice and a driver corresponding to the sensor; and deploying the streaming microservice with the driver corresponding to the sensor.
    Type: Grant
    Filed: September 9, 2019
    Date of Patent: January 5, 2021
    Assignee: Shanghai United Imaging Intelligence Co., Ltd.
    Inventors: Arun Innanje, Abhishek Sharma, Ziyan Wu, Terrence Chen
  • Patent number: 10824832
    Abstract: Barcode tag conditions on sample tubes are detected utilizing side view images of sample tubes for streamlining handling in clinical laboratory automation systems. The condition of the tags may be classified into classes, each divided into a list of additional subcategories that cover individual characteristics of the tag quality. According to an embodiment, a tube characterization station (TCS) is utilized to obtain the side view images. The TCS enables the simultaneous or near-simultaneous collection of three images for each tube, resulting in a 360 degree side view for each tube. The method is based on a supervised scene understanding concept, resulting in an explanation of each pixel into its semantic meaning. Two parallel low-level cues for condition recognition, in combination with a tube model extraction cue, may be utilized. The semantic scene information is then integrated into a mid-level representation for final decision making into one of the condition classes.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: November 3, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Wen Wu, Benjamin Pollack, Terrence Chen
  • Patent number: 10816538
    Abstract: A model-based method of inspecting a specimen for presence of an interferent (H, I, and/or L). The method includes capturing images of the specimen at multiple different exposures times and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, identifying a serum or plasma portion of the specimen, and classifying whether an interferent is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: October 27, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack, Patrick Wissmann
  • Patent number: 10803619
    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: Grant
    Filed: March 13, 2017
    Date of Patent: October 13, 2020
    Assignee: Siemens Mobility GmbH
    Inventors: Kai Ma, Shanhui Sun, Stefan Kluckner, Ziyan Wu, Terrence Chen, Jan Ernst
  • Publication number: 20200297237
    Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.
    Type: Application
    Filed: March 22, 2019
    Publication date: September 24, 2020
    Inventors: Birgi Tamersoy, Vivek Kumar Singh, Kai Ma, Terrence Chen, Andreas Wimmer
  • Patent number: 10783655
    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: Grant
    Filed: April 11, 2018
    Date of Patent: September 22, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Zhuokai Zhao, Yao-jen Chang, Ruhan Sa, Kai Ma, Jianping Wang, Vivek Kumar Singh, Terrence Chen, Andreas Wimmer, Birgi Tamersoy
  • Publication number: 20200271507
    Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.
    Type: Application
    Filed: February 25, 2019
    Publication date: August 27, 2020
    Inventors: Ruhan Sa, Birgi Tamersoy, Yao-jen Chang, Klaus J. Kirchberg, Vivek Kumar Singh, Terrence Chen
  • Publication number: 20200265263
    Abstract: A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.
    Type: Application
    Filed: July 25, 2018
    Publication date: August 20, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Kai Ma, Vivek Singh, Terrence Chen, Benjamin S. Pollack
  • Patent number: 10748034
    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: Grant
    Filed: January 10, 2018
    Date of Patent: August 18, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Vivek Kumar Singh, Klaus J. Kirchberg, Kai Ma, Yao-jen Chang, Terrence Chen
  • Patent number: 10746665
    Abstract: A model-based method of inspecting a specimen for presence of one or more artifacts (e.g., a clot, bubble, and/or foam). The method includes capturing multiple images of the specimen at multiple different exposures and at multiple spectra having different nominal wavelengths, selection of optimally-exposed pixels from the captured images to generate optimally-exposed image data for each spectra, computing statistics of the optimally-exposed pixels to generate statistical data, identifying a serum or plasma portion of the specimen, and classifying, based on the statistical data, whether an artifact is present or absent within the serum or plasma portion. Testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: August 18, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack
  • Patent number: 10746753
    Abstract: A model-based method of classifying a specimen in a specimen container. The method includes capturing images of the specimen and container at multiple different exposures times, at multiple different spectra having different nominal wavelengths, and at different viewpoints by using multiple cameras. From the captured images, 2D data sets are generated. The 2D data sets are based upon selection of optimally-exposed pixels from the multiple different exposure images to generate optimally-exposed image data for each spectra. Based upon these 2D data sets, various components are classified using a multi-class classifier, such as serum or plasma portion, settled blood portion, gel separator (if present), tube, air, or label. From the classification data and 2D data sets, a 3D model can be generated. Specimen testing apparatus and quality check modules adapted to carry out the method are described, as are other aspects.
    Type: Grant
    Filed: January 24, 2017
    Date of Patent: August 18, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack
  • Patent number: 10725060
    Abstract: A method of tube slot localization is provided using a tray coordinate system and a camera coordinate system. The method includes receiving, a series of images from at least one camera of a tray comprising tube slots arranged in a matrix of rows and columns. Each tube slot is configured to receive a sample tube. The method also includes automatically detecting fiducial markers disposed on cross sectional areas between the tube slots on the tray and receiving an encoder value indicating when each row of the tray is substantially at the center of the camera's field of view. The method further includes determining calibration information to provide mapping of locations from the tray coordinate system to locations from the camera coordinate system and automatically aligning the tray based on the encoder value and calibration information.
    Type: Grant
    Filed: February 16, 2016
    Date of Patent: July 28, 2020
    Assignee: Siemens Healthcare Diagnostics Inc.
    Inventors: Yao-Jen Chang, Patrick Wissmann, Wen Wu, Guillaume Dumont, Benjamin Pollack, Terrence Chen
  • Publication number: 20200232908
    Abstract: Embodiments provide a method of using image-based tube top circle detection based on multiple candidate selection to localize the tube top circle region in input images. According to embodiments provided herein, the multi-candidate selection enhances the robustness of tube circle detection by making use of multiple views of the same tube to improve the robustness of tube top circle detection. With multiple candidates extracted from images under different viewpoints of the same tube, the multi-candidate selection algorithm selects an optimal combination among the candidates and provides more precise measurement of tube characteristics. This information is invaluable in an IVD environment in which a sample handler is processing the tubes and moving the tubes to analyzers for testing and analysis.
    Type: Application
    Filed: June 25, 2018
    Publication date: July 23, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Yao-Jen Chang, Stefan Kluckner, Benjamin S. Pollack, Terrence Chen
  • Patent number: 10716530
    Abstract: An automation method is disclosed for an X-ray tube scanner having an X-ray tube and an X-ray detector. The method allows the X-ray tube scanner to detect the X-ray detector's plane with an object to be imaged placed on the X-ray detector; determine a boundary box of the object to be imaged on the X-ray detector; determine the object's center position and orientation on the X-ray detector's plane; transfer the object's center position from the object's coordinate system to the X-ray tube's coordinate system; and estimate the X-ray tube control parameters for aligning the X-ray field emitted from the X-ray tube's collimator to the object's center position and the object's orientation on the X-ray detector.
    Type: Grant
    Filed: April 11, 2018
    Date of Patent: July 21, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Yao-jen Chang, Birgi Tamersoy, Susanne Oepping, Ralf Nanke, Terrence Chen
  • Patent number: 10699438
    Abstract: The present embodiments relate to localizing a mobile device in a complex, three-dimensional scene. By way of introduction, the present embodiments described below include apparatuses and methods for using multiple, independent pose estimations to increase the accuracy of a single, resulting pose estimation. The present embodiments increase the amount of input data by windowing a single depth image, using multiple depth images from the same sensor, and/or using multiple depth image from different sensors. The resulting pose estimation uses the input data with a multi-window model, a multi-shot model, a multi-sensor model, or a combination thereof to accurately estimate the pose of a mobile device.
    Type: Grant
    Filed: July 6, 2017
    Date of Patent: June 30, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Oliver Lehmann, Stefan Kluckner, Terrence Chen
  • Publication number: 20200175307
    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: Application
    Filed: February 11, 2020
    Publication date: June 4, 2020
    Inventors: Ali Kamen, Shanhui Sun, Terrence Chen, Tommaso Mansi, Alexander Michael Gigler, Patra Charalampaki, Maximilian Fleischer, Dorin Comaniciu
  • Publication number: 20200167591
    Abstract: Methods for image-based detection of the tops of sample tubes used in an automated diagnostic analysis system may be based on a convolutional neural network to pre-process images of the sample tube tops to intensify the tube top circle edges while suppressing the edge response from other objects that may appear in the image. Edge maps generated by the methods may be used for various image-based sample tube analyses, categorizations, and/or characterizations of the sample tubes to control a robot in relationship to the sample tubes. Image processing and control apparatus configured to carry out the methods are also described, as are other aspects.
    Type: Application
    Filed: June 25, 2018
    Publication date: May 28, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Yao-Jen Chang, Stefan Kluckner, Benjamin S. Pollack, Terrence Chen
  • Publication number: 20200158745
    Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for Hemolysis, Icterus, and/or Lipemia, or Normal detection. The method captures one or more images of a labeled specimen container including a serum or plasma portion, processes the one or more images to provide segmentation data and identification of a label-containing region, and classifying the label-containing region with a convolutional neural network (CNN) to provide a pixel-by-pixel (or patch-by-patch) characterization of the label thickness count, which may be used to adjust intensities of regions of a serum or plasma portion having label occlusion. Optionally, the CNN can characterize the label-containing region as one of multiple pre-defined label configurations. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.
    Type: Application
    Filed: April 13, 2017
    Publication date: May 21, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Jiang Tian, Stefan Kluckner, Shanhui Sun, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack
  • Publication number: 20200151498
    Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization may be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a specimen. The method includes capturing one or more images of a labeled specimen container including a serum or plasma portion, processing the one or more images with a convolutional neural network to provide a determination of Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N). In further embodiments, the convolutional neural network can provide N-Class segmentation information. Quality check modules and testing apparatus adapted to carry out the method are described, as are other aspects.
    Type: Application
    Filed: April 10, 2018
    Publication date: May 14, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Shanhui SUN, Stefan KLUCKNER, Yao-Jen CHANG, Terrence CHEN, Benjamin S. POLLACK
  • Publication number: 20200151878
    Abstract: A method of characterizing a serum and plasma portion of a specimen in regions occluded by one or more labels. The characterization method may be used to provide input to an HILN (H, I, and/or L, or N) detection method. The characterization method includes capturing one or more images of a labeled specimen container including a serum or plasma portion from multiple viewpoints, processing the one or more images to provide segmentation data including identification of a label-containing region, determining a closest label match of the label-containing region to a reference label configuration selected from a reference label configuration database, and generating a combined representation based on the segmentation information and the closest label match. Using the combined representation allows for compensation of the light blocking effects of the label-containing region. Quality check modules and testing apparatus and adapted to carry out the method are described, as are other aspects.
    Type: Application
    Filed: April 10, 2018
    Publication date: May 14, 2020
    Applicant: Siemens Healthcare Diagnostics Inc.
    Inventors: Stefan Kluckner, Patrick Wissmann, Yao-Jen Chang, Terrence Chen, Benjamin S. Pollack